BMZ-OPNG Test

gistfile1.txt

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Auswertung GMZ"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import statsmodels.formula.api as smf\n",
      "import statsmodels as sm\n",
      "import pandas as pd\n",
      "import pymc as pm\n",
      "from scipy import stats\n",
      "import itertools"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Einige Parameter. Siehe Kursskript"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# optical density of original suspension\n",
      "od_600 = .5\n",
      "volume = 1.7 # ml\n",
      "\n",
      "# reaction time [s]\n",
      "t = 10 * 60"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Einlesen der Ergebnisse der einzelnen Kurse"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classes = {}\n",
      "\n",
      "for klass in \"ABCDEF\":\n",
      "    p = pd.read_csv(\"./ONPG-Test BMZ 2013_{}.csv\".format(klass),\n",
      "                    decimal=',', na_values=[\"\"], )\n",
      "    p = p.set_index([\"strain\", \"medium\"])\n",
      "    p = p.stack().dropna()\n",
      "    p.index.rename([\"strain\", \"medium\", \"group\"], inplace=True)\n",
      "    p = p.clip_lower(0.001) # ?????\n",
      "    p = p.apply(np.log)\n",
      "    p = pd.DataFrame(p, columns=[\"od_420\"])\n",
      "    p['volume'] = 1.7\n",
      "    p['beta_gal'] = 1e2 * p['od_420'] * p['volume'] / (t * od_600)\n",
      "    classes[klass] = p\n",
      "\n",
      "\n",
      "df_lac = pd.concat(classes)\n",
      "#df_lac.loc['B', 'A', 'LB', '2'] = -3 # work around bug\n",
      "#df_lac.loc['B', 'A', 'LB', '3'] = -3.5\n",
      "df_lac"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "MultiIndex: 603 entries, (A, A, LB, 1) to (F, F, LB_IPTG_GLU, 7)\n",
        "Data columns (total 3 columns):\n",
        "od_420      603  non-null values\n",
        "volume      603  non-null values\n",
        "beta_gal    603  non-null values\n",
        "dtypes: float64(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 603 entries, (A, A, LB, 1) to (F, F, LB_IPTG_GLU, 7)\n",
        "Data columns (total 3 columns):\n",
        "od_420      603  non-null values\n",
        "volume      603  non-null values\n",
        "beta_gal    603  non-null values\n",
        "dtypes: float64(3)"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Erster Index: Kursnummer\n",
      "- Zweiter Index: Bakterienstamm\n",
      "- Dritter Index: Medium\n",
      "- Vierter Index: Gruppennummer\n",
      "\n",
      "`df_lac.ix['B', 'A', 'LB', 2]['od_420']` ist also das Ergebnis von Kurs Gruppe 2\n",
      "bei Kurs 'B' f\u00fcr den Stamm A.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = df_lac.xs('beta_gal', axis=1)#.apply(np.exp)\n",
      "axes = d.unstack(level='strain').reset_index('medium').boxplot(by='medium', rot=30, grid=False)\n",
      "axes[0].figure.set_size_inches(12, 4)\n",
      "axes[0].figure.suptitle(\"\")\n",
      "axes[0].set_ylabel('log of reaction speed')\n",
      "for ax in axes:\n",
      "    ax.set_xlabel(\"\")\n",
      "ax.figure.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEZCAYAAACUzbT7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nALQSjRUAAAAAtNL/ByAYcai3/frbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6700590>"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Rough estimates for location and std in log-domain"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "groups = df_lac.xs('beta_gal', axis=1).groupby(level=['strain', 'medium'], axis=0)\n",
      "grid = True\n",
      "\n",
      "fig = plt.figure(figsize=(10.3, 13))\n",
      "ax = fig.add_subplot(412)\n",
      "groups.agg(lambda x: sm.robust.norms.estimate_location(x, sm.robust.scale.stand_mad(x))).unstack().plot(kind='bar', ax=ax, grid=grid)\n",
      "ax.set_title('loc')\n",
      "\n",
      "ax = fig.add_subplot(413)\n",
      "groups.agg(lambda x: sm.robust.scale.stand_mad(x)).unstack().plot(kind='bar', ax=ax, grid=grid)\n",
      "ax.set_title('mad')\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAAHpCAYAAAC1AzvXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7e6ab50>"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Gaussian mixture model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def build_model_vars(name, values, outlier_prob):\n",
      "    \"\"\" Build dristribution objects for a gaussian mixture model of data\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    \n",
      "    name : str\n",
      "        postfix for variable names\n",
      "    \n",
      "    values : ndarray\n",
      "        1d array of values to model\n",
      "        \n",
      "    outlier_prob : pymc.Distribution\n",
      "        a-prioriy distribution of parameter for bernoulli var\n",
      "        that determines if a value is an outlier\n",
      "    \n",
      "    Return\n",
      "    ------\n",
      "    \n",
      "    dict of names to distributions\n",
      "    \"\"\"\n",
      "    values = np.asarray(values)\n",
      "    \n",
      "    # Some estimtes for the parameters. For faster\n",
      "    # convergence only.\n",
      "    num_values = len(values)\n",
      "    data_loc = np.median(values)\n",
      "    data_scale = sm.robust.stand_mad(values)\n",
      "    guess_outlier = np.abs(values - data_loc) > 1.5 * data_scale\n",
      "    \n",
      "    outlier = pm.Bernoulli('outlier_' + name, p=outlier_prob, size=num_values,\n",
      "                           value=guess_outlier)\n",
      "    outlier_sim = pm.Bernoulli('outlier_sim_' + name, p=outlier_prob)\n",
      "    \n",
      "    outlier_dist = pm.Normal(\"outlier_dist_\" + name, mu=0,\n",
      "                             tau=2. ** -2, size=num_values, plot=False)\n",
      "    outlier_dist_sim = pm.Normal(\"outlier_dist_sim_\" + name, mu=0,\n",
      "                                 tau=2. ** -2)\n",
      "    \n",
      "    true_val = pm.Uniform('true_val_' + name, lower=-6, upper=4,\n",
      "                          value=data_loc)\n",
      "    \n",
      "    mean = outlier * outlier_dist + true_val\n",
      "    mean_sim = outlier_sim * outlier_dist_sim + true_val\n",
      "    std = pm.Uniform('std_' + name, lower=0, upper=5,\n",
      "                     value=data_scale)\n",
      "    \n",
      "    msd_val = pm.Normal('msd_val_' + name, mu=mean, tau=1/std ** 2,\n",
      "                        value=values, observed=True)\n",
      "    msd_val_sim = pm.Normal('msd_val_sim_' + name, mu=mean_sim,\n",
      "                            tau=std ** -2)\n",
      "    \n",
      "    vars = {'outlier': outlier,\n",
      "            'outlier_dist': outlier_dist,\n",
      "            'true_val': true_val,\n",
      "            'mean': mean,\n",
      "            'std': std,\n",
      "            'msd_val': msd_val,\n",
      "            'msd_val_sim': msd_val_sim}\n",
      "    \n",
      "    return vars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "burn = 20000\n",
      "iters = burn + 70000\n",
      "N = iters - burn\n",
      "\n",
      "vals = df_lac.unstack([0, -1])['beta_gal']\n",
      "\n",
      "# create an empty data frame to hold the samples\n",
      "# of the posterior distribution of the location parameter\n",
      "index_vals = itertools.product(*vals.index.levels)\n",
      "\n",
      "index = pd.MultiIndex.from_tuples(\n",
      "    list(index_vals), \n",
      "    names=['strain', 'medium']\n",
      ")\n",
      "df_dist = pd.DataFrame(index=range(N), columns=index)\n",
      "\n",
      "outlier_prob = pm.Beta('outlier_prob', alpha=1, beta=3)\n",
      "distributions = {}\n",
      "\n",
      "for sample_idx in vals.index:\n",
      "    values = vals.loc[sample_idx].dropna()\n",
      "    model_vars = build_model_vars(str(sample_idx), values, outlier_prob)\n",
      "    distributions[sample_idx] = model_vars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = pm.Model(sum((d.values() for d in distributions.values()), []) + [outlier_prob])\n",
      "mcmc = pm.MCMC(model)\n",
      "mcmc.sample(iters, burn=burn, thin=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "[****************100%******************]  90000 of 90000 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_dist = pd.DataFrame(index=range(N / 5), columns=index)\n",
      "\n",
      "for sample_idx in vals.index:\n",
      "    true_dist = distributions[sample_idx]['true_val']\n",
      "    sample = mcmc.trace(true_dist).gettrace()\n",
      "    df_dist[sample_idx] = sample"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Results for group F\n",
      "\n",
      "in the log-domain"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# only group F\n",
      "df_dist.groupby(level=['strain'], axis=1).plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 319,
       "text": [
        "strain\n",
        "A         Axes(0.125,0.125;0.775x0.775)\n",
        "B         Axes(0.125,0.125;0.775x0.775)\n",
        "C         Axes(0.125,0.125;0.775x0.775)\n",
        "D         Axes(0.125,0.125;0.775x0.775)\n",
        "E         Axes(0.125,0.125;0.775x0.775)\n",
        "F         Axes(0.125,0.125;0.775x0.775)\n",
        "dtype: object"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAECCAYAAADgnZClAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/X83rD2zm418rAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3da06e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAECCAYAAAD9z2x7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3da06b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAECCAYAAAAPX/ubAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL4XVqpkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3dab4ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAECCAYAAAAYfWtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3db0a110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAECCAYAAAAYfWtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nENKPUREghJB+jIoAIYT0Y1QECCGkH/v/L2rA63X5WQoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3db29f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAECCAYAAAAVYxsVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3d90d550>"
       ]
      }
     ],
     "prompt_number": 319
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Results for all groups\n",
      "\n",
      "in the log-domain"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# all groups!!!!\n",
      "df_dist.groupby(level=['strain'], axis=1).plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 313,
       "text": [
        "strain\n",
        "A         Axes(0.125,0.125;0.775x0.775)\n",
        "B         Axes(0.125,0.125;0.775x0.775)\n",
        "C         Axes(0.125,0.125;0.775x0.775)\n",
        "D         Axes(0.125,0.125;0.775x0.775)\n",
        "E         Axes(0.125,0.125;0.775x0.775)\n",
        "F         Axes(0.125,0.125;0.775x0.775)\n",
        "dtype: object"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAECCAYAAAA/0+q6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPEQYY4x5xkOEMcaYZ/8AJ6fqZx/NhG8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2de8e310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAECCAYAAAAYfWtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLJPTtbbRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2d701c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAECCAYAAAARlssoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nygAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2db07a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAECCAYAAADkaECYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQn6hAEEII8en/B/8xUL98Ar39AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2f3b92d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAECCAYAAAAVYxsVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzoEgCIIIg5wDQRAEEQY5B4IgCCKM/w8TQy+6lbKBlwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2f3de410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAECCAYAAADkaECYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSEggCIIgiJCQQBAEQRAh+f8t9BAAufWgvwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2f5a9710>"
       ]
      }
     ],
     "prompt_number": 313
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Stuff (Model checking??)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcmc.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "outlier_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.002            0.048            0.002                  [ 0.  0.]\n",
        "\t0.015            0.123            0.006                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.152            0.359            0.03                   [ 0.  1.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.002            0.045            0.001                  [ 0.  0.]\n",
        "\t0.002            0.048            0.002                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.001            0.033            0.001                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.007            0.083            0.004                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.999            0.03             0.001                  [ 1.  1.]\n",
        "\t0.002            0.04             0.002                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.002            0.049            0.002                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('A', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.24             0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('F', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.026            1.984            0.016            [-4.015  3.72 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.897           -1.324          0.046          1.372         3.866\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('A', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.007           1.999            0.014            [-3.834  3.942]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.91            -1.345          -0.016         1.333         3.877\n",
        "\t\n",
        "\n",
        "outlier_('F', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.001            0.034            0.001                  [ 0.  0.]\n",
        "\t0.006            0.074            0.003                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.005            0.067            0.003                  [ 0.  0.]\n",
        "\t0.008            0.091            0.004                  [ 0.  0.]\n",
        "\t0.025            0.157            0.007                  [ 0.  0.]\n",
        "\t0.002            0.04             0.001                  [ 0.  0.]\n",
        "\t0.002            0.045            0.002                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.01             0.102            0.006                  [ 0.  0.]\n",
        "\t0.002            0.04             0.001                  [ 0.  0.]\n",
        "\t0.907            0.29             0.024                  [ 0.  1.]\n",
        "\t0.006            0.08             0.004                  [ 0.  0.]\n",
        "\t0.002            0.047            0.001                  [ 0.  0.]\n",
        "\t0.014            0.116            0.004                  [ 0.  0.]\n",
        "\t0.021            0.145            0.007                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.018            0.135            0.007                  [ 0.  0.]\n",
        "\t0.002            0.045            0.001                  [ 0.  0.]\n",
        "\t0.006            0.076            0.004                  [ 0.  0.]\n",
        "\t0.025            0.155            0.009                  [ 0.  0.]\n",
        "\t0.0              0.012            0.0                    [ 0.  0.]\n",
        "\t0.006            0.075            0.003                  [ 0.  0.]\n",
        "\t0.004            0.065            0.003                  [ 0.  0.]\n",
        "\t0.0              0.012            0.0                    [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.01             0.099            0.005                  [ 0.  0.]\n",
        "\t0.014            0.119            0.006                  [ 0.  0.]\n",
        "\t0.002            0.043            0.001                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.008            0.089            0.004                  [ 0.  0.]\n",
        "\t0.003            0.053            0.002                  [ 0.  0.]\n",
        "\t0.007            0.086            0.003                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "true_val_('D', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.471            0.017            0.0              [ 0.438  0.504]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.437            0.46            0.471          0.482         0.503\n",
        "\t\n",
        "\n",
        "outlier_('E', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.01             0.099            0.004                  [ 0.  0.]\n",
        "\t0.01             0.101            0.004                  [ 0.  0.]\n",
        "\t0.009            0.095            0.004                  [ 0.  0.]\n",
        "\t0.011            0.106            0.004                  [ 0.  0.]\n",
        "\t0.005            0.068            0.002                  [ 0.  0.]\n",
        "\t0.01             0.099            0.004                  [ 0.  0.]\n",
        "\t0.007            0.085            0.003                  [ 0.  0.]\n",
        "\t0.014            0.119            0.005                  [ 0.  0.]\n",
        "\t0.015            0.123            0.005                  [ 0.  0.]\n",
        "\t0.017            0.13             0.006                  [ 0.  0.]\n",
        "\t0.027            0.161            0.008                  [ 0.  0.]\n",
        "\t0.013            0.114            0.004                  [ 0.  0.]\n",
        "\t0.077            0.267            0.011                  [ 0.  1.]\n",
        "\t0.03             0.171            0.009                  [ 0.  0.]\n",
        "\t0.021            0.144            0.006                  [ 0.  0.]\n",
        "\t0.15             0.357            0.019                  [ 0.  1.]\n",
        "\t0.023            0.15             0.01                   [ 0.  0.]\n",
        "\t0.011            0.105            0.004                  [ 0.  0.]\n",
        "\t0.046            0.21             0.012                  [ 0.  0.]\n",
        "\t0.208            0.406            0.021                  [ 0.  1.]\n",
        "\t0.032            0.177            0.009                  [ 0.  0.]\n",
        "\t0.013            0.111            0.005                  [ 0.  0.]\n",
        "\t0.063            0.243            0.015                  [ 0.  1.]\n",
        "\t0.977            0.15             0.007                  [ 1.  1.]\n",
        "\t0.013            0.112            0.005                  [ 0.  0.]\n",
        "\t0.012            0.11             0.005                  [ 0.  0.]\n",
        "\t0.018            0.133            0.008                  [ 0.  0.]\n",
        "\t0.014            0.119            0.006                  [ 0.  0.]\n",
        "\t0.011            0.107            0.004                  [ 0.  0.]\n",
        "\t0.008            0.091            0.003                  [ 0.  0.]\n",
        "\t0.015            0.121            0.006                  [ 0.  0.]\n",
        "\t0.002            0.048            0.001                  [ 0.  0.]\n",
        "\t0.005            0.068            0.002                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('D', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.239            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "std_('B', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.761            0.108            0.003            [ 0.559  0.975]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.577            0.685           0.752          0.827         1.003\n",
        "\t\n",
        "\n",
        "true_val_('D', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.072            0.02             0.0              [ 0.034  0.112]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.033            0.058           0.071          0.085         0.112\n",
        "\t\n",
        "\n",
        "outlier_sim_('B', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.24             0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "true_val_('B', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.341            0.019            0.0              [ 0.302  0.378]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.303            0.328           0.34           0.353         0.379\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('D', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.003            1.992            0.016            [-3.873  3.905]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.873           -1.326          0.006          1.334         3.905\n",
        "\t\n",
        "\n",
        "true_val_('C', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.232           0.103            0.003            [-2.438 -2.032]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.437           -2.298          -2.232         -2.166        -2.03\n",
        "\t\n",
        "\n",
        "std_('F', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.14             0.02             0.0              [ 0.104  0.18 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.107            0.126           0.138          0.152         0.185\n",
        "\t\n",
        "\n",
        "outlier_('C', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.014            0.119            0.004                  [ 0.  0.]\n",
        "\t0.007            0.083            0.003                  [ 0.  0.]\n",
        "\t0.015            0.12             0.005                  [ 0.  0.]\n",
        "\t0.021            0.143            0.006                  [ 0.  0.]\n",
        "\t0.02             0.141            0.006                  [ 0.  0.]\n",
        "\t0.047            0.211            0.009                  [ 0.  0.]\n",
        "\t0.891            0.312            0.024                  [ 0.  1.]\n",
        "\t0.011            0.104            0.003                  [ 0.  0.]\n",
        "\t0.035            0.184            0.011                  [ 0.  0.]\n",
        "\t0.02             0.14             0.006                  [ 0.  0.]\n",
        "\t0.011            0.104            0.004                  [ 0.  0.]\n",
        "\t0.015            0.123            0.007                  [ 0.  0.]\n",
        "\t0.013            0.115            0.004                  [ 0.  0.]\n",
        "\t0.011            0.104            0.004                  [ 0.  0.]\n",
        "\t0.017            0.13             0.008                  [ 0.  0.]\n",
        "\t0.011            0.106            0.004                  [ 0.  0.]\n",
        "\t0.422            0.494            0.032                  [ 0.  1.]\n",
        "\t0.536            0.499            0.037                  [ 0.  1.]\n",
        "\t0.021            0.142            0.006                  [ 0.  0.]\n",
        "\t0.006            0.077            0.003                  [ 0.  0.]\n",
        "\t0.013            0.113            0.004                  [ 0.  0.]\n",
        "\t0.021            0.145            0.007                  [ 0.  0.]\n",
        "\t0.009            0.094            0.003                  [ 0.  0.]\n",
        "\t0.08             0.271            0.013                  [ 0.  1.]\n",
        "\t0.014            0.116            0.004                  [ 0.  0.]\n",
        "\t0.034            0.182            0.01                   [ 0.  0.]\n",
        "\t0.019            0.138            0.005                  [ 0.  0.]\n",
        "\t0.082            0.275            0.016                  [ 0.  1.]\n",
        "\t0.008            0.088            0.003                  [ 0.  0.]\n",
        "\t0.01             0.097            0.004                  [ 0.  0.]\n",
        "\t0.03             0.169            0.007                  [ 0.  0.]\n",
        "\t0.009            0.096            0.003                  [ 0.  0.]\n",
        "\t0.012            0.11             0.004                  [ 0.  0.]\n",
        "\t0.008            0.087            0.003                  [ 0.  0.]\n",
        "\t0.011            0.104            0.004                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            1.0           1.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "true_val_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.513            0.008            0.0              [ 0.497  0.528]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.497            0.507           0.513          0.518         0.528\n",
        "\t\n",
        "\n",
        "outlier_dist_('A', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.015            2.018            0.056            [-4.032  3.904]\n",
        "\t-0.035           1.992            0.044            [-3.724  4.04 ]\n",
        "\t-0.004           1.962            0.067            [-3.912  3.9  ]\n",
        "\t-0.049           1.94             0.066            [-3.861  3.912]\n",
        "\t0.304            1.974            0.058            [-3.553  4.052]\n",
        "\t-0.022           2.206            0.22             [-3.504  3.268]\n",
        "\t0.047            1.951            0.156            [-3.673  4.055]\n",
        "\t-0.024           1.924            0.069            [-3.766  3.774]\n",
        "\t0.035            2.062            0.171            [-3.789  4.136]\n",
        "\t0.32             1.947            0.168            [-3.462  4.426]\n",
        "\t-0.732           2.015            0.173            [-4.692  3.13 ]\n",
        "\t0.142            2.028            0.134            [-3.599  4.284]\n",
        "\t-0.046           1.981            0.038            [-3.922  3.751]\n",
        "\t0.122            1.844            0.138            [-3.431  3.626]\n",
        "\t0.203            2.0              0.167            [-3.97   3.678]\n",
        "\t-0.142           2.006            0.079            [-3.985  3.927]\n",
        "\t0.128            1.983            0.133            [-3.954  4.015]\n",
        "\t0.262            1.866            0.129            [-3.554  3.769]\n",
        "\t0.113            0.278            0.027            [-0.405  0.678]\n",
        "\t0.173            2.016            0.106            [-3.58   4.402]\n",
        "\t0.027            1.934            0.06             [-3.798  3.679]\n",
        "\t0.063            1.992            0.041            [-3.804  4.012]\n",
        "\t0.151            1.911            0.116            [-3.551  3.89 ]\n",
        "\t-0.179           2.006            0.162            [-4.094  3.825]\n",
        "\t-0.083           1.994            0.048            [-3.995  3.722]\n",
        "\t0.01             1.901            0.115            [-3.629  3.82 ]\n",
        "\t0.322            2.039            0.172            [-3.493  4.454]\n",
        "\t-0.128           2.092            0.079            [-4.327  3.983]\n",
        "\t0.669            1.803            0.16             [-2.771  4.022]\n",
        "\t0.218            1.997            0.147            [-3.781  3.902]\n",
        "\t0.068            2.015            0.106            [-4.126  3.963]\n",
        "\t-0.175           1.988            0.125            [-4.217  3.615]\n",
        "\t0.217            2.1              0.151            [-3.717  4.464]\n",
        "\t-0.117           2.035            0.158            [-3.988  4.24 ]\n",
        "\t-0.1             1.936            0.111            [-4.005  3.671]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.94            -1.334          0.049          1.365         4.013\n",
        "\t-3.88            -1.393          -0.047         1.297         3.907\n",
        "\t-3.878           -1.279          0.005          1.276         3.993\n",
        "\t-3.854           -1.327          -0.103         1.234         3.924\n",
        "\t-3.745           -1.027          0.464          1.705         3.905\n",
        "\t-3.551           -2.183          0.639          1.944         3.238\n",
        "\t-3.858           -1.248          -0.057         1.391         3.947\n",
        "\t-3.766           -1.301          -0.066         1.284         3.777\n",
        "\t-3.949           -1.394          0.019          1.42          4.043\n",
        "\t-3.465           -0.951          0.24           1.513         4.426\n",
        "\t-4.522           -2.126          -0.836         0.61          3.332\n",
        "\t-3.792           -1.212          0.102          1.504         4.104\n",
        "\t-3.876           -1.382          -0.029         1.287         3.815\n",
        "\t-3.366           -1.148          0.078          1.419         3.718\n",
        "\t-4.062           -1.145          0.438          1.629         3.647\n",
        "\t-4.021           -1.52           -0.219         1.219         3.912\n",
        "\t-3.827           -1.129          0.218          1.399         4.166\n",
        "\t-3.507           -0.955          0.261          1.552         3.844\n",
        "\t-0.452           -0.065          0.122          0.299         0.644\n",
        "\t-3.763           -1.191          0.184          1.47          4.292\n",
        "\t-3.689           -1.306          0.009          1.343         3.824\n",
        "\t-3.788           -1.259          0.052          1.362         4.065\n",
        "\t-3.659           -1.157          0.167          1.496         3.785\n",
        "\t-3.854           -1.562          -0.322         1.094         4.14\n",
        "\t-3.967           -1.412          -0.074         1.248         3.767\n",
        "\t-3.653           -1.269          0.013          1.27          3.81\n",
        "\t-3.247           -1.062          0.226          1.558         4.844\n",
        "\t-4.322           -1.491          -0.133         1.261         4.017\n",
        "\t-2.648           -0.698          0.644          1.961         4.203\n",
        "\t-3.826           -1.088          0.37           1.607         3.868\n",
        "\t-3.921           -1.249          0.044          1.374         4.206\n",
        "\t-4.28            -1.503          -0.097         1.171         3.573\n",
        "\t-4.087           -1.205          0.218          1.717         4.206\n",
        "\t-4.259           -1.453          -0.153         1.135         4.045\n",
        "\t-3.886           -1.363          -0.14          1.142         3.883\n",
        "\t\n",
        "\n",
        "true_val_('E', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.071           0.063            0.001            [-2.196 -1.949]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.192           -2.114          -2.072         -2.03         -1.945\n",
        "\t\n",
        "\n",
        "outlier_dist_('C', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.023           1.99             0.088            [-3.933  3.986]\n",
        "\t0.332            2.029            0.152            [-4.062  4.049]\n",
        "\t0.1              1.912            0.064            [-3.817  3.797]\n",
        "\t0.066            1.98             0.065            [-3.665  4.086]\n",
        "\t0.274            1.471            0.137            [-2.351  3.34 ]\n",
        "\t0.045            2.053            0.149            [-4.117  4.015]\n",
        "\t0.105            1.989            0.106            [-3.639  4.142]\n",
        "\t0.059            2.045            0.182            [-4.17   4.167]\n",
        "\t-0.002           1.964            0.055            [-4.063  3.593]\n",
        "\t-0.518           2.007            0.156            [-4.619  3.402]\n",
        "\t0.023            1.974            0.077            [-3.909  3.892]\n",
        "\t-0.005           1.899            0.059            [-3.894  3.521]\n",
        "\t-0.067           1.982            0.061            [-3.92   3.852]\n",
        "\t0.406            1.998            0.097            [-3.29   4.536]\n",
        "\t-0.072           1.902            0.111            [-3.791  3.796]\n",
        "\t-0.506           1.969            0.159            [-4.182  3.558]\n",
        "\t0.139            1.941            0.064            [-3.911  3.577]\n",
        "\t-0.166           2.033            0.172            [-4.248  3.524]\n",
        "\t-0.079           2.079            0.137            [-4.412  3.906]\n",
        "\t-0.093           2.005            0.064            [-3.998  3.82 ]\n",
        "\t-0.087           2.097            0.146            [-4.319  3.77 ]\n",
        "\t0.13             1.972            0.072            [-3.753  4.053]\n",
        "\t0.049            1.923            0.069            [-3.82   3.783]\n",
        "\t0.002            2.05             0.112            [-3.84   4.108]\n",
        "\t0.257            1.966            0.175            [-3.377  4.057]\n",
        "\t-0.062           1.917            0.052            [-3.886  3.633]\n",
        "\t0.68             1.302            0.13             [-1.446  3.142]\n",
        "\t0.093            1.96             0.048            [-3.603  4.159]\n",
        "\t0.047            1.957            0.073            [-3.827  3.804]\n",
        "\t-0.282           2.117            0.193            [-3.924  5.112]\n",
        "\t-0.724           2.173            0.205            [-5.052  3.192]\n",
        "\t-0.027           1.994            0.077            [-3.952  3.934]\n",
        "\t0.32             1.953            0.153            [-3.485  3.879]\n",
        "\t0.008            1.933            0.047            [-3.919  3.674]\n",
        "\t0.314            1.915            0.075            [-3.696  3.767]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.922           -1.303          -0.085         1.243         4.03\n",
        "\t-3.731           -1.024          0.323          1.678         4.521\n",
        "\t-3.772           -1.144          0.108          1.377         3.869\n",
        "\t-3.88            -1.293          0.094          1.419         3.884\n",
        "\t-2.275           -0.746          0.149          1.172         3.457\n",
        "\t-4.004           -1.326          0.047          1.373         4.17\n",
        "\t-3.706           -1.218          0.042          1.435         4.098\n",
        "\t-4.609           -1.135          0.079          1.331         3.885\n",
        "\t-3.913           -1.32           0.068          1.325         3.794\n",
        "\t-4.515           -1.793          -0.509         0.743         3.528\n",
        "\t-3.835           -1.328          0.03           1.342         4.033\n",
        "\t-3.752           -1.267          0.024          1.243         3.678\n",
        "\t-3.92            -1.382          -0.094         1.245         3.852\n",
        "\t-3.499           -0.921          0.445          1.697         4.377\n",
        "\t-3.865           -1.332          -0.03          1.181         3.731\n",
        "\t-4.527           -1.78           -0.526         0.812         3.355\n",
        "\t-3.911           -1.166          0.3            1.532         3.581\n",
        "\t-4.101           -1.502          -0.172         1.164         3.693\n",
        "\t-4.1             -1.468          -0.109         1.248         4.279\n",
        "\t-4.067           -1.443          -0.08          1.282         3.775\n",
        "\t-4.048           -1.52           -0.131         1.361         4.123\n",
        "\t-3.776           -1.159          0.189          1.412         4.044\n",
        "\t-3.759           -1.204          0.021          1.325         3.866\n",
        "\t-3.758           -1.352          -0.097         1.279         4.262\n",
        "\t-3.415           -1.058          0.206          1.625         4.038\n",
        "\t-3.76            -1.323          -0.103         1.19          3.781\n",
        "\t-1.411           -0.351          0.466          1.803         3.214\n",
        "\t-3.783           -1.196          0.103          1.377         4.005\n",
        "\t-3.635           -1.29           -0.002         1.348         4.007\n",
        "\t-4.206           -1.623          -0.389         0.938         4.858\n",
        "\t-4.964           -2.116          -0.826         0.932         3.355\n",
        "\t-3.952           -1.343          -0.022         1.283         3.934\n",
        "\t-3.47            -1.089          0.36           1.753         3.91\n",
        "\t-3.721           -1.306          -0.001         1.312         3.881\n",
        "\t-3.58            -0.972          0.476          1.627         3.895\n",
        "\t\n",
        "\n",
        "outlier_dist_('C', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.117            2.115            0.137            [-3.995  4.191]\n",
        "\t-0.075           1.957            0.107            [-4.142  3.508]\n",
        "\t-0.313           2.046            0.174            [-4.221  3.839]\n",
        "\t0.04             2.08             0.107            [-4.102  3.964]\n",
        "\t-0.128           1.976            0.141            [-3.923  3.888]\n",
        "\t-0.009           2.036            0.104            [-4.293  3.776]\n",
        "\t-1.077           1.434            0.077            [-3.227  2.727]\n",
        "\t-0.299           1.964            0.183            [-4.305  3.281]\n",
        "\t0.013            1.855            0.128            [-3.645  3.862]\n",
        "\t0.111            2.052            0.072            [-3.781  4.268]\n",
        "\t0.019            1.89             0.15             [-3.681  3.835]\n",
        "\t-0.263           0.824            0.08             [-1.898  1.037]\n",
        "\t0.134            2.024            0.157            [-3.443  4.661]\n",
        "\t1.305            0.463            0.045            [ 0.571  2.147]\n",
        "\t-0.058           2.004            0.094            [-3.98   3.819]\n",
        "\t-0.905           1.895            0.183            [-4.609  3.796]\n",
        "\t0.018            1.9              0.102            [-3.64   3.825]\n",
        "\t0.341            1.895            0.101            [-4.079  3.779]\n",
        "\t1.366            0.895            0.067            [-0.538  3.087]\n",
        "\t-0.067           2.007            0.075            [-3.992  3.794]\n",
        "\t-0.02            2.013            0.114            [-3.937  4.118]\n",
        "\t-0.046           2.054            0.106            [-3.932  4.006]\n",
        "\t0.148            2.005            0.08             [-3.935  4.016]\n",
        "\t-0.065           1.902            0.1              [-3.631  4.103]\n",
        "\t-0.045           2.076            0.143            [-4.092  4.102]\n",
        "\t0.541            2.048            0.174            [-3.397  4.943]\n",
        "\t0.07             1.717            0.163            [-3.523  3.161]\n",
        "\t0.017            1.963            0.095            [-4.006  3.797]\n",
        "\t0.151            2.145            0.182            [-4.412  4.307]\n",
        "\t-0.123           1.971            0.075            [-3.841  3.853]\n",
        "\t-0.024           2.386            0.213            [-4.915  4.187]\n",
        "\t0.006            2.05             0.128            [-4.078  4.104]\n",
        "\t-0.519           2.509            0.246            [-5.171  4.052]\n",
        "\t0.129            1.829            0.161            [-3.361  3.715]\n",
        "\t0.589            1.637            0.063            [-3.053  3.647]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.929           -1.264          0.073          1.475         4.338\n",
        "\t-3.96            -1.367          -0.078         1.223         3.753\n",
        "\t-4.185           -1.77           -0.372         1.04          3.951\n",
        "\t-4.0             -1.417          0.068          1.482         4.099\n",
        "\t-4.081           -1.411          -0.112         1.119         3.819\n",
        "\t-4.255           -1.307          0.084          1.341         3.845\n",
        "\t-3.016           -1.852          -1.464         -0.843        2.964\n",
        "\t-4.127           -1.758          -0.133         1.076         3.638\n",
        "\t-4.042           -1.142          0.194          1.153         3.576\n",
        "\t-3.883           -1.285          0.114          1.454         4.208\n",
        "\t-3.724           -1.133          -0.09          1.232         3.809\n",
        "\t-1.857           -0.8            -0.202         0.389         1.098\n",
        "\t-4.29            -1.157          0.109          1.546         4.041\n",
        "\t0.554            0.91            1.237          1.719         2.133\n",
        "\t-4.046           -1.404          -0.02          1.283         3.801\n",
        "\t-3.604           -1.862          -1.474         -0.783        5.159\n",
        "\t-3.841           -1.296          0.196          1.212         3.67\n",
        "\t-4.111           -0.681          0.749          1.386         3.77\n",
        "\t-1.103           1.079           1.482          1.85          2.739\n",
        "\t-3.96            -1.415          -0.056         1.3           3.829\n",
        "\t-3.952           -1.281          -0.05          1.231         4.116\n",
        "\t-3.931           -1.473          -0.08          1.323         4.007\n",
        "\t-3.726           -1.186          0.126          1.45          4.249\n",
        "\t-3.774           -1.247          -0.27          1.111         3.997\n",
        "\t-4.163           -1.362          -0.028         1.294         4.055\n",
        "\t-3.588           -0.768          0.478          1.847         4.81\n",
        "\t-3.094           -1.202          0.051          1.149         3.858\n",
        "\t-3.871           -1.287          -0.015         1.334         3.969\n",
        "\t-4.282           -1.194          0.123          1.603         4.495\n",
        "\t-3.853           -1.502          -0.169         1.214         3.849\n",
        "\t-4.721           -1.586          0.018          1.675         4.548\n",
        "\t-4.134           -1.329          -0.028         1.396         4.064\n",
        "\t-5.295           -2.615          -0.203         1.355         3.98\n",
        "\t-3.545           -0.983          0.057          1.254         3.647\n",
        "\t-3.266           -0.303          1.005          1.54          3.467\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('F', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.009            1.988            0.015            [-3.822  3.886]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.825           -1.346          0.005          1.375         3.884\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('C', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.047            2.015            0.016            [-4.041  3.826]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.938           -1.316          0.058          1.427         3.962\n",
        "\t\n",
        "\n",
        "outlier_sim_('E', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.064            0.245            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('B', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.027           2.002            0.019            [-3.825  4.03 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.018           -1.362          -0.015         1.337         3.892\n",
        "\t\n",
        "\n",
        "outlier_('F', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.001            0.037            0.001                  [ 0.  0.]\n",
        "\t0.009            0.094            0.004                  [ 0.  0.]\n",
        "\t0.046            0.21             0.011                  [ 0.  0.]\n",
        "\t0.006            0.076            0.003                  [ 0.  0.]\n",
        "\t0.091            0.288            0.018                  [ 0.  1.]\n",
        "\t0.01             0.099            0.005                  [ 0.  0.]\n",
        "\t0.003            0.053            0.002                  [ 0.  0.]\n",
        "\t0.045            0.208            0.011                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.001            0.037            0.001                  [ 0.  0.]\n",
        "\t0.01             0.101            0.004                  [ 0.  0.]\n",
        "\t0.029            0.167            0.008                  [ 0.  0.]\n",
        "\t0.007            0.083            0.005                  [ 0.  0.]\n",
        "\t0.006            0.075            0.003                  [ 0.  0.]\n",
        "\t0.004            0.067            0.003                  [ 0.  0.]\n",
        "\t0.007            0.083            0.004                  [ 0.  0.]\n",
        "\t0.011            0.105            0.004                  [ 0.  0.]\n",
        "\t0.003            0.057            0.002                  [ 0.  0.]\n",
        "\t0.005            0.07             0.003                  [ 0.  0.]\n",
        "\t0.004            0.062            0.002                  [ 0.  0.]\n",
        "\t0.002            0.039            0.001                  [ 0.  0.]\n",
        "\t0.007            0.081            0.003                  [ 0.  0.]\n",
        "\t0.001            0.036            0.001                  [ 0.  0.]\n",
        "\t0.003            0.05             0.002                  [ 0.  0.]\n",
        "\t0.017            0.128            0.006                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.011            0.104            0.004                  [ 0.  0.]\n",
        "\t0.004            0.063            0.002                  [ 0.  0.]\n",
        "\t0.007            0.082            0.004                  [ 0.  0.]\n",
        "\t0.003            0.054            0.002                  [ 0.  0.]\n",
        "\t0.006            0.079            0.004                  [ 0.  0.]\n",
        "\t0.004            0.063            0.002                  [ 0.  0.]\n",
        "\t0.004            0.067            0.003                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('D', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.153            1.583            0.149            [-2.899  3.239]\n",
        "\t0.228            0.904            0.089            [-1.441  1.96 ]\n",
        "\t-0.213           0.959            0.094            [-1.873  1.568]\n",
        "\t0.836            1.733            0.171            [-2.966  3.671]\n",
        "\t2.865            0.669            0.066            [ 1.54   3.907]\n",
        "\t-0.673           0.141            0.014            [-0.914 -0.426]\n",
        "\t0.63             0.45             0.044            [-0.169  1.479]\n",
        "\t0.428            0.464            0.046            [-0.487  1.171]\n",
        "\t-1.256           2.292            0.226            [-5.451  2.853]\n",
        "\t-3.029           1.149            0.114            [-4.876 -1.383]\n",
        "\t-1.557           2.244            0.224            [-5.121  1.872]\n",
        "\t0.29             2.544            0.251            [-4.676  4.981]\n",
        "\t-1.574           1.401            0.138            [-4.027  1.266]\n",
        "\t-0.512           0.04             0.001            [-0.592 -0.433]\n",
        "\t-0.513           0.039            0.001            [-0.589 -0.433]\n",
        "\t0.161            1.262            0.123            [-1.912  2.557]\n",
        "\t-0.234           0.041            0.002            [-0.318 -0.154]\n",
        "\t-1.304           2.124            0.21             [-5.757  2.024]\n",
        "\t-0.901           1.603            0.157            [-4.408  1.65 ]\n",
        "\t2.315            0.256            0.025            [ 1.808  2.762]\n",
        "\t0.993            0.109            0.011            [ 0.854  1.219]\n",
        "\t-0.283           0.04             0.001            [-0.361 -0.203]\n",
        "\t0.12             1.465            0.14             [-2.864  2.859]\n",
        "\t-2.172           0.204            0.02             [-2.545 -1.867]\n",
        "\t-0.222           0.039            0.001            [-0.3   -0.147]\n",
        "\t-1.791           2.441            0.243            [-5.016  3.538]\n",
        "\t-1.3             0.444            0.043            [-2.263 -0.526]\n",
        "\t1.829            0.347            0.034            [ 1.157  2.493]\n",
        "\t-0.563           0.039            0.003            [-0.636 -0.482]\n",
        "\t1.677            1.054            0.104            [ 0.05   3.502]\n",
        "\t2.161            0.118            0.011            [ 1.926  2.387]\n",
        "\t0.082            1.657            0.157            [-3.048  3.451]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.829           -0.926          0.16           1.249         3.372\n",
        "\t-1.419           -0.374          0.16           0.88          1.99\n",
        "\t-1.932           -0.905          -0.198         0.548         1.529\n",
        "\t-2.945           -0.133          0.569          2.283         3.707\n",
        "\t1.654            2.376           2.893          3.322         4.334\n",
        "\t-0.937           -0.781          -0.673         -0.562        -0.436\n",
        "\t-0.196           0.27            0.656          0.959         1.462\n",
        "\t-0.386           0.016           0.408          0.795         1.382\n",
        "\t-5.425           -2.936          -1.471         0.511         2.903\n",
        "\t-4.727           -4.1            -3.156         -1.933        -1.008\n",
        "\t-5.26            -3.252          -2.377         0.717         1.799\n",
        "\t-5.377           -1.491          0.642          2.287         4.711\n",
        "\t-4.051           -2.649          -1.579         -0.649        1.249\n",
        "\t-0.592           -0.538          -0.512         -0.487        -0.433\n",
        "\t-0.59            -0.537          -0.513         -0.488        -0.433\n",
        "\t-2.042           -0.862          0.087          1.162         2.477\n",
        "\t-0.323           -0.26           -0.232         -0.205        -0.158\n",
        "\t-5.693           -2.697          -0.827         0.235         2.099\n",
        "\t-4.314           -1.807          -0.7           0.243         1.788\n",
        "\t1.823            2.108           2.356          2.503         2.779\n",
        "\t0.849            0.905           0.958          1.072         1.214\n",
        "\t-0.362           -0.308          -0.282         -0.257        -0.204\n",
        "\t-2.969           -0.833          0.138          1.086         2.786\n",
        "\t-2.541           -2.369          -2.115         -2.006        -1.856\n",
        "\t-0.299           -0.247          -0.222         -0.196        -0.146\n",
        "\t-4.777           -3.854          -2.359         -0.212        4.0\n",
        "\t-2.267           -1.572          -1.251         -0.997        -0.527\n",
        "\t1.233            1.591           1.775          2.012         2.675\n",
        "\t-0.644           -0.588          -0.561         -0.536        -0.489\n",
        "\t0.051            0.771           1.563          2.658         3.505\n",
        "\t1.924            2.087           2.154          2.248         2.385\n",
        "\t-3.194           -0.979          0.117          1.184         3.356\n",
        "\t\n",
        "\n",
        "outlier_dist_('D', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.382           1.986            0.148            [-4.361  3.638]\n",
        "\t-0.089           2.179            0.164            [-4.183  4.259]\n",
        "\t0.101            1.911            0.154            [-3.488  3.858]\n",
        "\t-0.174           2.013            0.133            [-4.115  3.733]\n",
        "\t-0.16            1.946            0.097            [-4.053  3.52 ]\n",
        "\t0.175            2.073            0.109            [-3.957  4.317]\n",
        "\t0.086            1.928            0.172            [-3.795  3.412]\n",
        "\t0.907            1.389            0.134            [-1.553  3.752]\n",
        "\t0.488            1.905            0.18             [-3.192  3.987]\n",
        "\t-0.103           1.908            0.138            [-3.631  3.557]\n",
        "\t-0.208           1.879            0.158            [-3.734  3.69 ]\n",
        "\t-0.034           1.692            0.158            [-3.467  3.306]\n",
        "\t-0.794           0.977            0.096            [-2.831  1.148]\n",
        "\t0.003            2.208            0.206            [-4.151  4.192]\n",
        "\t-0.437           0.129            0.012            [-0.685 -0.262]\n",
        "\t0.0              1.931            0.073            [-3.854  3.709]\n",
        "\t0.01             1.786            0.177            [-2.733  3.762]\n",
        "\t0.204            0.251            0.025            [-0.399  0.564]\n",
        "\t-0.029           2.021            0.096            [-4.059  3.845]\n",
        "\t0.12             1.89             0.155            [-3.717  3.734]\n",
        "\t-0.031           1.9              0.112            [-3.65   3.707]\n",
        "\t0.435            1.571            0.138            [-2.189  4.045]\n",
        "\t-0.128           2.025            0.127            [-4.019  3.74 ]\n",
        "\t0.308            2.114            0.184            [-3.935  5.034]\n",
        "\t-0.124           1.936            0.184            [-3.995  3.703]\n",
        "\t0.054            2.044            0.114            [-4.134  3.939]\n",
        "\t0.355            1.869            0.158            [-3.28   4.132]\n",
        "\t0.48             1.993            0.187            [-3.412  3.975]\n",
        "\t-0.65            0.089            0.001            [-0.823 -0.474]\n",
        "\t-1.007           0.554            0.054            [-1.924  0.032]\n",
        "\t-0.512           2.025            0.162            [-4.424  3.828]\n",
        "\t2.539            1.588            0.156            [-0.877  5.035]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.981           -1.713          -0.473         0.774         4.174\n",
        "\t-4.384           -1.563          -0.072         1.424         4.112\n",
        "\t-3.372           -1.223          0.097          1.331         4.053\n",
        "\t-4.066           -1.57           -0.216         1.222         3.792\n",
        "\t-3.961           -1.525          -0.169         1.194         3.661\n",
        "\t-4.073           -1.174          0.206          1.544         4.247\n",
        "\t-3.756           -1.238          0.247          1.505         3.465\n",
        "\t-1.447           0.009           0.743          1.63          3.917\n",
        "\t-3.18            -0.733          0.503          1.8           4.017\n",
        "\t-3.575           -1.487          -0.17          1.194         3.649\n",
        "\t-3.819           -1.49           -0.297         1.019         3.638\n",
        "\t-3.232           -1.092          -0.17          1.084         3.567\n",
        "\t-2.823           -1.48           -0.822         -0.098        1.158\n",
        "\t-3.958           -1.569          -0.104         1.509         4.473\n",
        "\t-0.623           -0.506          -0.451         -0.389        0.012\n",
        "\t-3.838           -1.293          0.025          1.316         3.765\n",
        "\t-2.814           -1.395          -0.237         1.153         3.7\n",
        "\t-0.409           0.09            0.249          0.396         0.556\n",
        "\t-3.947           -1.4            -0.049         1.342         3.978\n",
        "\t-3.676           -1.097          0.102          1.372         3.779\n",
        "\t-3.755           -1.33           -0.014         1.281         3.621\n",
        "\t-2.539           -0.442          0.182          1.466         3.798\n",
        "\t-3.899           -1.528          -0.158         1.173         3.902\n",
        "\t-4.212           -0.981          0.299          1.533         4.883\n",
        "\t-3.906           -1.422          -0.146         1.055         3.831\n",
        "\t-3.959           -1.327          0.05           1.422         4.135\n",
        "\t-3.293           -0.879          0.306          1.506         4.131\n",
        "\t-3.45            -1.003          0.548          2.029         3.951\n",
        "\t-0.824           -0.709          -0.65          -0.592        -0.475\n",
        "\t-1.87            -1.435          -1.074         -0.613        0.129\n",
        "\t-4.304           -1.819          -0.581         0.74          4.069\n",
        "\t-0.783           1.267           2.746          3.761         5.163\n",
        "\t\n",
        "\n",
        "msd_val_sim_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.515            0.497            0.004            [ 0.17   1.135]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.051            0.484           0.512          0.54          1.044\n",
        "\t\n",
        "\n",
        "std_('B', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.108            0.015            0.0              [ 0.081  0.136]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.084            0.097           0.106          0.117         0.14\n",
        "\t\n",
        "\n",
        "msd_val_sim_('B', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.334            0.488            0.004            [-0.034  0.733]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.151           0.263           0.342          0.421         0.692\n",
        "\t\n",
        "\n",
        "msd_val_sim_('C', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.38            0.639            0.006            [-3.392 -1.295]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.455           -2.654          -2.379         -2.1          -1.324\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('A', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.007            1.994            0.018            [-3.817  3.964]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.873           -1.331          0.003          1.348         3.923\n",
        "\t\n",
        "\n",
        "msd_val_sim_('C', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.906           0.976            0.009            [-4.836 -1.075]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.806           -3.485          -2.899         -2.335        -1.036\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('E', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.005            1.994            0.015            [-3.896  3.973]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.958           -1.33           0.015          1.348         3.933\n",
        "\t\n",
        "\n",
        "msd_val_sim_('C', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.227           0.741            0.008            [-3.654 -0.89 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.637           -2.573          -2.223         -1.873        -0.85\n",
        "\t\n",
        "\n",
        "outlier_dist_('E', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.068           1.944            0.127            [-3.916  3.708]\n",
        "\t0.018            1.92             0.148              [-3.97  3.74]\n",
        "\t-0.175           2.019            0.183            [-4.045  3.909]\n",
        "\t0.102            1.955            0.062            [-3.744  3.905]\n",
        "\t0.019            1.966            0.064            [-3.849  3.864]\n",
        "\t-0.097           1.94             0.168            [-3.459  3.705]\n",
        "\t-0.033           2.034            0.064            [-4.024  3.891]\n",
        "\t0.149            2.017            0.086            [-3.737  4.273]\n",
        "\t0.12             2.031            0.084            [-3.845  4.088]\n",
        "\t-0.034           2.002            0.07             [-4.192  3.765]\n",
        "\t0.17             1.894            0.078            [-3.578  3.811]\n",
        "\t0.054            1.941            0.063            [-3.799  3.876]\n",
        "\t0.023            1.864            0.055            [-3.613  3.778]\n",
        "\t0.839            1.42             0.139            [-2.156  3.447]\n",
        "\t-0.043           1.951            0.052            [-3.977  3.671]\n",
        "\t0.324            1.681            0.108            [-3.299  3.567]\n",
        "\t-0.061           2.076            0.098            [-4.164  4.013]\n",
        "\t-0.019           1.996            0.054            [-3.967  3.794]\n",
        "\t-1.454           1.062            0.106            [-3.13   0.316]\n",
        "\t0.311            1.838            0.094            [-3.59   3.973]\n",
        "\t0.046            1.934            0.062            [-3.736  3.835]\n",
        "\t0.086            1.876            0.116            [-3.594  3.816]\n",
        "\t0.6              1.686            0.155            [-2.612  3.743]\n",
        "\t1.402            0.391            0.015            [ 0.714  2.093]\n",
        "\t0.035            2.015            0.084            [-3.872  3.968]\n",
        "\t-0.161           1.892            0.16             [-3.986  3.188]\n",
        "\t0.42             2.07             0.124            [-3.62   4.502]\n",
        "\t-0.093           2.104            0.151            [-4.079  4.428]\n",
        "\t0.07             1.989            0.113            [-3.913  4.02 ]\n",
        "\t0.198            2.016            0.112            [-3.781  3.986]\n",
        "\t0.043            2.034            0.055            [-3.986  4.095]\n",
        "\t-0.29            2.365            0.233            [-3.906  3.664]\n",
        "\t-0.184           2.29             0.188            [-4.458  4.45 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.042           -1.302          -0.033         1.24          3.619\n",
        "\t-3.993           -1.205          0.052          1.273         3.722\n",
        "\t-5.227           -1.331          -0.062         1.184         3.469\n",
        "\t-3.779           -1.179          0.127          1.392         3.9\n",
        "\t-3.834           -1.293          0.01           1.328         3.912\n",
        "\t-3.539           -1.544          -0.166         1.334         3.636\n",
        "\t-3.999           -1.408          -0.063         1.333         3.946\n",
        "\t-3.858           -1.2            0.155          1.482         4.176\n",
        "\t-4.079           -1.17           0.161          1.533         3.918\n",
        "\t-4.052           -1.338          -0.006         1.267         3.923\n",
        "\t-3.576           -1.098          0.235          1.428         3.823\n",
        "\t-3.67            -1.266          0.007          1.339         4.034\n",
        "\t-3.727           -1.168          0.143          1.158         3.688\n",
        "\t-2.158           -0.049          0.742          1.867         3.447\n",
        "\t-3.94            -1.336          -0.006         1.285         3.775\n",
        "\t-3.396           -0.662          0.523          1.335         3.508\n",
        "\t-4.118           -1.482          -0.041         1.347         4.093\n",
        "\t-3.911           -1.371          -0.054         1.318         3.875\n",
        "\t-3.154           -2.333          -1.642         -0.421        0.307\n",
        "\t-3.665           -0.739          0.566          1.262         3.939\n",
        "\t-3.805           -1.193          0.007          1.355         3.784\n",
        "\t-3.724           -1.135          0.064          1.392         3.752\n",
        "\t-2.633           -0.566          0.499          1.908         3.737\n",
        "\t0.688            1.191           1.417          1.644         2.077\n",
        "\t-3.829           -1.338          -0.003         1.38          4.02\n",
        "\t-4.2             -1.355          -0.021         1.161         3.073\n",
        "\t-3.824           -0.867          0.389          1.799         4.371\n",
        "\t-4.079           -1.459          -0.135         1.145         4.459\n",
        "\t-3.842           -1.212          0.029          1.328         4.135\n",
        "\t-3.809           -1.175          0.266          1.589         3.975\n",
        "\t-4.017           -1.293          0.064          1.392         4.081\n",
        "\t-3.961           -2.384          -0.623         1.98          3.63\n",
        "\t-4.228           -1.802          -0.285         1.111         4.763\n",
        "\t\n",
        "\n",
        "msd_val_sim_('D', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.47             0.498            0.005            [-0.029  0.826]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.004           0.408           0.471          0.532         0.865\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('C', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.009           2.001            0.016            [-3.947  3.895]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.972           -1.35           -0.014         1.337         3.889\n",
        "\t\n",
        "\n",
        "true_val_('F', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.263            0.026            0.0              [ 0.211  0.313]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.212            0.246           0.263          0.28          0.314\n",
        "\t\n",
        "\n",
        "outlier_('A', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.033            0.178            0.007                  [ 0.  0.]\n",
        "\t0.019            0.135            0.005                  [ 0.  0.]\n",
        "\t0.014            0.116            0.004                  [ 0.  0.]\n",
        "\t0.098            0.297            0.014                  [ 0.  1.]\n",
        "\t0.027            0.162            0.005                  [ 0.  0.]\n",
        "\t0.042            0.201            0.009                  [ 0.  0.]\n",
        "\t0.034            0.182            0.006                  [ 0.  0.]\n",
        "\t0.069            0.253            0.011                  [ 0.  1.]\n",
        "\t0.048            0.215            0.009                  [ 0.  0.]\n",
        "\t0.068            0.251            0.01                   [ 0.  1.]\n",
        "\t0.053            0.223            0.01                   [ 0.  1.]\n",
        "\t0.062            0.241            0.011                  [ 0.  1.]\n",
        "\t0.023            0.149            0.007                  [ 0.  0.]\n",
        "\t0.014            0.116            0.004                  [ 0.  0.]\n",
        "\t0.06             0.237            0.011                  [ 0.  1.]\n",
        "\t0.026            0.159            0.006                  [ 0.  0.]\n",
        "\t0.084            0.277            0.012                  [ 0.  1.]\n",
        "\t0.023            0.15             0.006                  [ 0.  0.]\n",
        "\t0.101            0.302            0.016                  [ 0.  1.]\n",
        "\t0.027            0.163            0.006                  [ 0.  0.]\n",
        "\t0.039            0.193            0.007                  [ 0.  0.]\n",
        "\t0.696            0.46             0.034                  [ 0.  1.]\n",
        "\t0.024            0.154            0.006                  [ 0.  0.]\n",
        "\t0.017            0.129            0.005                  [ 0.  0.]\n",
        "\t0.016            0.125            0.005                  [ 0.  0.]\n",
        "\t0.021            0.143            0.004                  [ 0.  0.]\n",
        "\t0.047            0.212            0.01                   [ 0.  0.]\n",
        "\t0.024            0.152            0.006                  [ 0.  0.]\n",
        "\t0.025            0.156            0.005                  [ 0.  0.]\n",
        "\t0.026            0.158            0.006                  [ 0.  0.]\n",
        "\t0.015            0.121            0.004                  [ 0.  0.]\n",
        "\t0.027            0.163            0.006                  [ 0.  0.]\n",
        "\t0.01             0.099            0.004                  [ 0.  0.]\n",
        "\t0.024            0.153            0.007                  [ 0.  0.]\n",
        "\t0.015            0.12             0.004                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "msd_val_sim_('B', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.62            0.932            0.009            [-4.373 -0.845]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.386           -3.166          -2.62          -2.092        -0.851\n",
        "\t\n",
        "\n",
        "outlier_dist_('B', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.315           2.12             0.151            [-4.414  3.946]\n",
        "\t0.091            2.013            0.066            [-3.829  4.033]\n",
        "\t0.045            2.075            0.072            [-3.841  4.273]\n",
        "\t0.005            1.845            0.139            [-3.536  3.722]\n",
        "\t-0.03            2.033            0.051            [-3.929  4.157]\n",
        "\t0.079            1.977            0.072            [-3.82   3.962]\n",
        "\t-0.109           1.921            0.145            [-3.947  3.478]\n",
        "\t-0.061           1.988            0.107            [-4.204  3.732]\n",
        "\t-0.124           1.966            0.105            [-4.022  3.542]\n",
        "\t-0.173           2.132            0.149            [-4.328  4.129]\n",
        "\t-0.344           2.065            0.166            [-4.188  4.239]\n",
        "\t-0.055           2.013            0.09             [-3.977  3.834]\n",
        "\t0.017            1.937            0.103            [-3.891  3.835]\n",
        "\t0.202            2.059            0.157            [-3.572  4.087]\n",
        "\t0.11             1.975            0.07             [-3.696  3.999]\n",
        "\t-0.123           1.866            0.135            [-3.617  3.702]\n",
        "\t0.012            1.958            0.055            [-3.699  3.778]\n",
        "\t-0.201           1.771            0.13             [-3.706  3.405]\n",
        "\t0.138            2.1              0.209            [-3.991  3.818]\n",
        "\t0.069            1.998            0.057            [-3.726  4.088]\n",
        "\t-1.429           1.038            0.103            [-2.965  0.407]\n",
        "\t0.002            1.991            0.082            [-3.855  3.837]\n",
        "\t-0.054           2.137            0.197            [-4.377  3.914]\n",
        "\t0.065            1.948            0.155            [-3.869  3.779]\n",
        "\t0.318            1.834            0.18             [-3.226  3.736]\n",
        "\t0.18             1.982            0.066            [-3.754  3.932]\n",
        "\t-0.164           1.968            0.13             [-3.804  3.91 ]\n",
        "\t0.133            2.047            0.183            [-3.609  4.207]\n",
        "\t-0.122           2.015            0.063            [-3.964  3.847]\n",
        "\t-0.033           2.031            0.092            [-4.047  3.897]\n",
        "\t0.087            2.021            0.061            [-3.779  4.007]\n",
        "\t0.008            1.868            0.12             [-3.642  3.702]\n",
        "\t-0.047           2.011            0.07             [-3.773  4.009]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.2             -1.738          -0.47          1.037         4.222\n",
        "\t-3.88            -1.237          0.096          1.472         3.99\n",
        "\t-4.112           -1.362          0.038          1.467         4.085\n",
        "\t-3.523           -1.225          -0.024         1.197         3.754\n",
        "\t-4.066           -1.411          -0.027         1.332         4.075\n",
        "\t-3.903           -1.251          0.067          1.426         3.908\n",
        "\t-3.868           -1.462          -0.138         1.254         3.586\n",
        "\t-4.021           -1.378          -0.04          1.239         3.937\n",
        "\t-3.851           -1.486          -0.162         1.209         3.774\n",
        "\t-4.183           -1.573          -0.155         1.073         4.353\n",
        "\t-4.35            -1.678          -0.45          0.906         4.105\n",
        "\t-3.93            -1.439          -0.017         1.286         3.922\n",
        "\t-3.857           -1.236          0.133          1.23          3.887\n",
        "\t-4.024           -1.202          0.25           1.683         3.835\n",
        "\t-3.679           -1.234          0.102          1.454         4.031\n",
        "\t-3.686           -1.366          -0.167         1.1           3.666\n",
        "\t-3.675           -1.309          -0.012         1.32          3.815\n",
        "\t-3.662           -1.36           -0.212         0.946         3.493\n",
        "\t-3.881           -1.142          0.093          1.441         3.978\n",
        "\t-3.807           -1.292          0.022          1.417         4.039\n",
        "\t-3.02            -2.316          -1.666         -0.531        0.371\n",
        "\t-3.843           -1.405          0.003          1.381         3.869\n",
        "\t-4.362           -1.603          -0.072         1.544         3.955\n",
        "\t-3.718           -1.251          0.1            1.313         3.942\n",
        "\t-3.178           -1.024          0.31           1.666         3.795\n",
        "\t-3.692           -1.174          0.195          1.53          4.054\n",
        "\t-3.867           -1.539          -0.185         1.09          3.866\n",
        "\t-3.546           -1.298          -0.084         1.55          4.298\n",
        "\t-3.985           -1.492          -0.127         1.244         3.829\n",
        "\t-3.98            -1.443          -0.035         1.337         3.988\n",
        "\t-3.831           -1.296          0.114          1.463         3.984\n",
        "\t-3.544           -1.219          -0.055         1.154         3.86\n",
        "\t-3.957           -1.412          -0.035         1.34          3.848\n",
        "\t\n",
        "\n",
        "true_val_('C', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.915           0.147            0.002            [-3.195 -2.63 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.201           -3.013          -2.914         -2.816        -2.634\n",
        "\t\n",
        "\n",
        "msd_val_sim_('D', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.072            0.501            0.004            [-0.391  0.527]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.412           -0.004          0.072          0.148         0.513\n",
        "\t\n",
        "\n",
        "true_val_('A', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.881           0.171            0.003            [-3.207 -2.53 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.22            -2.993          -2.884         -2.767        -2.541\n",
        "\t\n",
        "\n",
        "msd_val_sim_('F', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.269            0.534            0.004            [-0.445  0.845]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.366           0.162           0.265          0.367         0.969\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.007           1.988            0.017            [-3.73   4.049]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.869           -1.359          -0.012         1.326         3.934\n",
        "\t\n",
        "\n",
        "outlier_sim_('B', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.059            0.236            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "std_('C', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.512            0.136            0.01             [ 0.281  0.768]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.291            0.404           0.502          0.609         0.79\n",
        "\t\n",
        "\n",
        "outlier_prob:\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.062            0.013            0.001            [ 0.036  0.088]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.038            0.053           0.061          0.071         0.09\n",
        "\t\n",
        "\n",
        "true_val_('F', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.492            0.022            0.001            [ 0.449  0.537]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.447            0.478           0.493          0.506         0.535\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('F', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.009           1.995            0.017            [-4.092  3.709]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.923           -1.355          0.008          1.338         3.891\n",
        "\t\n",
        "\n",
        "std_('A', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.951            0.13             0.002            [ 0.723  1.219]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.733            0.859           0.94           1.028         1.238\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "msd_val_sim_('A', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.888           1.087            0.01             [-5.016 -0.795]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-5.011           -3.54           -2.893         -2.231        -0.784\n",
        "\t\n",
        "\n",
        "std_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.038            0.006            0.0              [ 0.027  0.051]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.027            0.034           0.037          0.042         0.052\n",
        "\t\n",
        "\n",
        "outlier_('C', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.049            0.215            0.008                  [ 0.  0.]\n",
        "\t0.034            0.182            0.007                  [ 0.  0.]\n",
        "\t0.024            0.153            0.005                  [ 0.  0.]\n",
        "\t0.019            0.136            0.004                  [ 0.  0.]\n",
        "\t0.049            0.215            0.008                  [ 0.  0.]\n",
        "\t0.027            0.163            0.007                  [ 0.  0.]\n",
        "\t0.058            0.234            0.009                  [ 0.  1.]\n",
        "\t0.038            0.192            0.008                  [ 0.  0.]\n",
        "\t0.04             0.196            0.007                  [ 0.  0.]\n",
        "\t0.037            0.19             0.007                  [ 0.  0.]\n",
        "\t0.04             0.196            0.007                  [ 0.  0.]\n",
        "\t0.028            0.164            0.006                  [ 0.  0.]\n",
        "\t0.046            0.21             0.008                  [ 0.  0.]\n",
        "\t0.077            0.267            0.012                  [ 0.  1.]\n",
        "\t0.046            0.21             0.008                  [ 0.  0.]\n",
        "\t0.061            0.239            0.011                  [ 0.  1.]\n",
        "\t0.15             0.357            0.016                  [ 0.  1.]\n",
        "\t0.021            0.142            0.006                  [ 0.  0.]\n",
        "\t0.03             0.169            0.006                  [ 0.  0.]\n",
        "\t0.028            0.165            0.007                  [ 0.  0.]\n",
        "\t0.017            0.127            0.004                  [ 0.  0.]\n",
        "\t0.029            0.168            0.006                  [ 0.  0.]\n",
        "\t0.022            0.147            0.005                  [ 0.  0.]\n",
        "\t0.045            0.207            0.008                  [ 0.  0.]\n",
        "\t0.049            0.216            0.009                  [ 0.  0.]\n",
        "\t0.044            0.204            0.008                  [ 0.  0.]\n",
        "\t0.044            0.205            0.01                   [ 0.  0.]\n",
        "\t0.039            0.194            0.008                  [ 0.  0.]\n",
        "\t0.04             0.196            0.008                  [ 0.  0.]\n",
        "\t0.02             0.14             0.004                  [ 0.  0.]\n",
        "\t0.008            0.09             0.002                  [ 0.  0.]\n",
        "\t0.026            0.16             0.006                  [ 0.  0.]\n",
        "\t0.036            0.187            0.006                  [ 0.  0.]\n",
        "\t0.024            0.153            0.005                  [ 0.  0.]\n",
        "\t0.185            0.388            0.018                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_('B', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.0              0.017            0.0                    [ 0.  0.]\n",
        "\t0.006            0.079            0.004                  [ 0.  0.]\n",
        "\t0.011            0.104            0.005                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.003            0.053            0.001                  [ 0.  0.]\n",
        "\t0.005            0.073            0.004                  [ 0.  0.]\n",
        "\t0.007            0.081            0.004                  [ 0.  0.]\n",
        "\t0.005            0.067            0.004                  [ 0.  0.]\n",
        "\t0.001            0.036            0.001                  [ 0.  0.]\n",
        "\t0.014            0.117            0.005                  [ 0.  0.]\n",
        "\t0.003            0.053            0.001                  [ 0.  0.]\n",
        "\t0.019            0.135            0.005                  [ 0.  0.]\n",
        "\t0.071            0.257            0.016                  [ 0.  1.]\n",
        "\t0.014            0.117            0.005                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.008            0.091            0.004                  [ 0.  0.]\n",
        "\t0.006            0.077            0.005                  [ 0.  0.]\n",
        "\t0.009            0.093            0.004                  [ 0.  0.]\n",
        "\t0.005            0.067            0.003                  [ 0.  0.]\n",
        "\t0.007            0.082            0.005                  [ 0.  0.]\n",
        "\t0.003            0.057            0.002                  [ 0.  0.]\n",
        "\t0.003            0.058            0.002                  [ 0.  0.]\n",
        "\t0.002            0.047            0.002                  [ 0.  0.]\n",
        "\t0.014            0.119            0.006                  [ 0.  0.]\n",
        "\t0.002            0.04             0.002                  [ 0.  0.]\n",
        "\t0.003            0.051            0.002                  [ 0.  0.]\n",
        "\t0.008            0.09             0.004                  [ 0.  0.]\n",
        "\t0.013            0.113            0.006                  [ 0.  0.]\n",
        "\t0.001            0.032            0.001                  [ 0.  0.]\n",
        "\t0.004            0.06             0.002                  [ 0.  0.]\n",
        "\t0.0              0.017            0.0                    [ 0.  0.]\n",
        "\t0.005            0.071            0.003                  [ 0.  0.]\n",
        "\t0.006            0.076            0.003                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('D', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.063            0.242            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "msd_val_sim_('F', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.492            0.518            0.005            [ 0.055  1.147]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.064           0.41            0.494          0.576         1.062\n",
        "\t\n",
        "\n",
        "outlier_sim_('D', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.24             0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('B', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.065            0.246            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_('B', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.086            0.28             0.013                  [ 0.  1.]\n",
        "\t0.07             0.256            0.012                  [ 0.  1.]\n",
        "\t0.029            0.169            0.006                  [ 0.  0.]\n",
        "\t0.073            0.26             0.012                  [ 0.  1.]\n",
        "\t0.04             0.196            0.007                  [ 0.  0.]\n",
        "\t0.024            0.152            0.005                  [ 0.  0.]\n",
        "\t0.026            0.159            0.006                  [ 0.  0.]\n",
        "\t0.097            0.297            0.012                  [ 0.  1.]\n",
        "\t0.03             0.171            0.008                  [ 0.  0.]\n",
        "\t0.024            0.154            0.005                  [ 0.  0.]\n",
        "\t0.032            0.175            0.009                  [ 0.  0.]\n",
        "\t0.044            0.205            0.01                   [ 0.  0.]\n",
        "\t0.057            0.232            0.008                  [ 0.  1.]\n",
        "\t0.025            0.155            0.006                  [ 0.  0.]\n",
        "\t0.029            0.169            0.007                  [ 0.  0.]\n",
        "\t0.024            0.152            0.006                  [ 0.  0.]\n",
        "\t0.027            0.163            0.005                  [ 0.  0.]\n",
        "\t0.03             0.17             0.006                  [ 0.  0.]\n",
        "\t0.033            0.18             0.008                  [ 0.  0.]\n",
        "\t0.096            0.295            0.014                  [ 0.  1.]\n",
        "\t0.022            0.146            0.006                  [ 0.  0.]\n",
        "\t0.025            0.157            0.007                  [ 0.  0.]\n",
        "\t0.067            0.249            0.012                  [ 0.  1.]\n",
        "\t0.038            0.192            0.007                  [ 0.  0.]\n",
        "\t0.242            0.428            0.024                  [ 0.  1.]\n",
        "\t0.121            0.327            0.015                  [ 0.  1.]\n",
        "\t0.035            0.183            0.007                  [ 0.  0.]\n",
        "\t0.032            0.176            0.007                  [ 0.  0.]\n",
        "\t0.018            0.131            0.004                  [ 0.  0.]\n",
        "\t0.021            0.145            0.006                  [ 0.  0.]\n",
        "\t0.037            0.189            0.007                  [ 0.  0.]\n",
        "\t0.034            0.182            0.009                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_('D', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.005            0.072            0.003                  [ 0.  0.]\n",
        "\t0.002            0.049            0.002                  [ 0.  0.]\n",
        "\t0.002            0.039            0.001                  [ 0.  0.]\n",
        "\t0.002            0.039            0.001                  [ 0.  0.]\n",
        "\t0.0              0.021            0.0                    [ 0.  0.]\n",
        "\t0.009            0.093            0.004                  [ 0.  0.]\n",
        "\t0.004            0.059            0.002                  [ 0.  0.]\n",
        "\t0.001            0.027            0.001                  [ 0.  0.]\n",
        "\t0.002            0.042            0.001                  [ 0.  0.]\n",
        "\t0.006            0.078            0.003                  [ 0.  0.]\n",
        "\t0.006            0.079            0.004                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.003            0.057            0.002                  [ 0.  0.]\n",
        "\t0.002            0.045            0.001                  [ 0.  0.]\n",
        "\t0.002            0.044            0.001                  [ 0.  0.]\n",
        "\t0.02             0.139            0.007                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.005            0.073            0.003                  [ 0.  0.]\n",
        "\t0.004            0.065            0.003                  [ 0.  0.]\n",
        "\t0.002            0.043            0.002                  [ 0.  0.]\n",
        "\t0.592            0.491            0.042                  [ 0.  1.]\n",
        "\t0.081            0.273            0.017                  [ 0.  1.]\n",
        "\t0.0              0.017            0.0                    [ 0.  0.]\n",
        "\t0.008            0.09             0.004                  [ 0.  0.]\n",
        "\t0.009            0.092            0.005                  [ 0.  0.]\n",
        "\t0.007            0.083            0.003                  [ 0.  0.]\n",
        "\t0.003            0.052            0.002                  [ 0.  0.]\n",
        "\t0.007            0.083            0.005                  [ 0.  0.]\n",
        "\t0.117            0.322            0.022                  [ 0.  1.]\n",
        "\t0.006            0.079            0.003                  [ 0.  0.]\n",
        "\t0.003            0.052            0.002                  [ 0.  0.]\n",
        "\t0.005            0.072            0.003                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_('B', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.0              0.008            0.0                    [ 0.  0.]\n",
        "\t0.003            0.051            0.002                  [ 0.  0.]\n",
        "\t0.006            0.077            0.004                  [ 0.  0.]\n",
        "\t0.003            0.05             0.001                  [ 0.  0.]\n",
        "\t0.008            0.089            0.005                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.001            0.027            0.001                  [ 0.  0.]\n",
        "\t0.005            0.068            0.003                  [ 0.  0.]\n",
        "\t0.0              0.008            0.0                    [ 0.  0.]\n",
        "\t0.008            0.091            0.004                  [ 0.  0.]\n",
        "\t0.004            0.061            0.002                  [ 0.  0.]\n",
        "\t0.012            0.108            0.005                  [ 0.  0.]\n",
        "\t0.474            0.499            0.042                  [ 0.  1.]\n",
        "\t0.009            0.092            0.004                  [ 0.  0.]\n",
        "\t0.007            0.086            0.004                  [ 0.  0.]\n",
        "\t0.012            0.109            0.006                  [ 0.  0.]\n",
        "\t0.001            0.022            0.0                    [ 0.  0.]\n",
        "\t0.001            0.022            0.0                    [ 0.  0.]\n",
        "\t0.004            0.061            0.002                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.01             0.101            0.004                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.004            0.063            0.004                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.001            0.025            0.001                  [ 0.  0.]\n",
        "\t0.001            0.033            0.001                  [ 0.  0.]\n",
        "\t0.011            0.103            0.007                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.0              0.008            0.0                    [ 0.  0.]\n",
        "\t0.004            0.062            0.002                  [ 0.  0.]\n",
        "\t0.002            0.047            0.002                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('C', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.009            1.996            0.017            [-4.014  3.758]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.846           -1.339          0.003          1.334         3.95\n",
        "\t\n",
        "\n",
        "std_('A', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.776            0.102            0.001            [ 0.59   0.978]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.605            0.703           0.766          0.837         1.005\n",
        "\t\n",
        "\n",
        "std_('F', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.062            0.009            0.0              [ 0.046  0.08 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.048            0.056           0.061          0.067         0.082\n",
        "\t\n",
        "\n",
        "msd_val_sim_('B', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.204           0.481            0.004            [-0.589  0.314]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.768           -0.256          -0.198         -0.139        0.179\n",
        "\t\n",
        "\n",
        "std_('C', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.394            0.077            0.005            [ 0.261  0.55 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.266            0.337           0.387          0.442         0.562\n",
        "\t\n",
        "\n",
        "true_val_('B', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.197           0.015            0.0              [-0.229 -0.169]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.227           -0.208          -0.198         -0.188        -0.166\n",
        "\t\n",
        "\n",
        "outlier_('E', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.015            0.12             0.007                  [ 0.  0.]\n",
        "\t0.005            0.073            0.004                  [ 0.  0.]\n",
        "\t0.006            0.08             0.003                  [ 0.  0.]\n",
        "\t0.003            0.057            0.002                  [ 0.  0.]\n",
        "\t0.013            0.114            0.006                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.031            0.173            0.008                  [ 0.  0.]\n",
        "\t0.006            0.078            0.003                  [ 0.  0.]\n",
        "\t0.005            0.073            0.002                  [ 0.  0.]\n",
        "\t0.007            0.086            0.003                  [ 0.  0.]\n",
        "\t0.061            0.24             0.013                  [ 0.  1.]\n",
        "\t0.002            0.045            0.001                  [ 0.  0.]\n",
        "\t0.02             0.139            0.006                  [ 0.  0.]\n",
        "\t0.003            0.056            0.003                  [ 0.  0.]\n",
        "\t0.007            0.085            0.003                  [ 0.  0.]\n",
        "\t0.006            0.075            0.003                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.01             0.097            0.006                  [ 0.  0.]\n",
        "\t0.014            0.117            0.006                  [ 0.  0.]\n",
        "\t0.0              0.021            0.0                    [ 0.  0.]\n",
        "\t0.01             0.1              0.003                  [ 0.  0.]\n",
        "\t0.005            0.073            0.003                  [ 0.  0.]\n",
        "\t0.006            0.078            0.003                  [ 0.  0.]\n",
        "\t0.003            0.052            0.002                  [ 0.  0.]\n",
        "\t0.009            0.095            0.004                  [ 0.  0.]\n",
        "\t0.003            0.054            0.003                  [ 0.  0.]\n",
        "\t0.003            0.057            0.002                  [ 0.  0.]\n",
        "\t0.011            0.105            0.004                  [ 0.  0.]\n",
        "\t0.004            0.061            0.002                  [ 0.  0.]\n",
        "\t0.006            0.079            0.003                  [ 0.  0.]\n",
        "\t0.009            0.094            0.005                  [ 0.  0.]\n",
        "\t0.014            0.119            0.007                  [ 0.  0.]\n",
        "\t0.012            0.11             0.006                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('A', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.042           2.047            0.094            [-4.04   4.025]\n",
        "\t-0.185           1.761            0.116            [-3.461  3.51 ]\n",
        "\t0.211            2.005            0.118            [-3.679  4.168]\n",
        "\t0.139            1.782            0.106            [-3.372  3.505]\n",
        "\t0.015            1.957            0.075            [-3.858  3.701]\n",
        "\t-0.213           1.84             0.123            [-4.029  3.058]\n",
        "\t0.086            1.921            0.063            [-3.776  3.789]\n",
        "\t-0.015           1.928            0.047            [-3.681  3.809]\n",
        "\t0.09             2.074            0.115            [-3.95   4.058]\n",
        "\t-0.268           2.077            0.144            [-4.607  3.578]\n",
        "\t0.319            1.526            0.147            [-3.101  2.784]\n",
        "\t0.013            1.941            0.131            [-3.461  4.027]\n",
        "\t0.076            2.013            0.057            [-3.825  4.035]\n",
        "\t-0.016           1.934            0.061            [-3.78   3.864]\n",
        "\t-0.278           0.182            0.018            [-0.565  0.05 ]\n",
        "\t0.008            1.954            0.04             [-3.738  3.889]\n",
        "\t-0.112           1.928            0.051            [-3.835  3.762]\n",
        "\t-0.136           1.98             0.092            [-3.916  3.928]\n",
        "\t-0.255           0.866            0.086            [-1.724  1.32 ]\n",
        "\t-0.802           0.999            0.099            [-2.61   0.766]\n",
        "\t0.02             1.96             0.045            [-3.807  3.891]\n",
        "\t1.159            1.693            0.143            [-2.963  3.476]\n",
        "\t0.13             2.064            0.176            [-3.663  4.35 ]\n",
        "\t0.248            2.022            0.18             [-3.569  3.997]\n",
        "\t0.075            1.974            0.124            [-3.911  3.97 ]\n",
        "\t0.008            1.971            0.049            [-3.79   3.939]\n",
        "\t0.096            1.948            0.065            [-3.642  4.072]\n",
        "\t-0.121           1.96             0.141            [-3.843  3.64 ]\n",
        "\t0.001            1.999            0.049            [-3.998  3.858]\n",
        "\t0.05             2.022            0.154            [-3.869  4.22 ]\n",
        "\t0.156            2.001            0.091            [-3.794  4.03 ]\n",
        "\t0.12             2.117            0.187            [-4.159  4.326]\n",
        "\t0.085            1.973            0.124            [-3.856  3.92 ]\n",
        "\t0.128            1.994            0.141            [-3.801  3.926]\n",
        "\t0.158            1.953            0.174            [-3.432  3.933]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.069           -1.369          0.006          1.262         4.008\n",
        "\t-3.63            -1.39           -0.205         0.947         3.347\n",
        "\t-3.861           -1.133          0.229          1.592         4.021\n",
        "\t-3.337           -1.088          0.227          1.322         3.563\n",
        "\t-3.751           -1.306          0.016          1.313         3.82\n",
        "\t-3.937           -1.464          -0.14          1.076         3.175\n",
        "\t-3.702           -1.199          0.082          1.37          3.906\n",
        "\t-3.636           -1.313          -0.129         1.305         3.88\n",
        "\t-3.951           -1.372          0.102          1.519         4.058\n",
        "\t-4.549           -1.507          -0.31          1.131         3.656\n",
        "\t-3.05            -0.715          0.447          1.448         2.866\n",
        "\t-3.583           -1.304          -0.13          1.298         3.938\n",
        "\t-3.947           -1.236          0.104          1.443         3.962\n",
        "\t-3.8             -1.332          -0.026         1.299         3.858\n",
        "\t-0.57            -0.421          -0.323         -0.107        0.047\n",
        "\t-3.789           -1.278          0.025          1.28          3.87\n",
        "\t-3.815           -1.387          -0.238         1.164         3.797\n",
        "\t-4.007           -1.454          -0.153         1.167         3.88\n",
        "\t-1.806           -0.894          -0.269         0.31          1.296\n",
        "\t-2.854           -1.567          -0.606         0.011         0.618\n",
        "\t-3.702           -1.294          -0.077         1.32          4.025\n",
        "\t-3.486           0.803           1.645          2.181         3.195\n",
        "\t-3.653           -1.355          0.113          1.497         4.369\n",
        "\t-3.486           -1.273          0.229          1.698         4.123\n",
        "\t-3.788           -1.242          0.014          1.354         4.156\n",
        "\t-3.789           -1.306          -0.012         1.277         3.94\n",
        "\t-3.812           -1.21           0.141          1.381         3.937\n",
        "\t-3.744           -1.501          -0.154         1.151         3.774\n",
        "\t-3.878           -1.331          0.0            1.323         4.016\n",
        "\t-3.919           -1.248          0.045          1.33          4.189\n",
        "\t-3.867           -1.208          0.185          1.522         3.992\n",
        "\t-4.115           -1.226          0.069          1.478         4.392\n",
        "\t-3.753           -1.24           0.07           1.387         4.066\n",
        "\t-3.864           -1.223          0.15           1.549         3.892\n",
        "\t-3.385           -1.172          0.015          1.441         4.03\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('B', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.026            2.011            0.017            [-3.795  4.066]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.881           -1.33           -0.001         1.38          3.994\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('A', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.008            1.992            0.016            [-3.811  3.896]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.867           -1.349          0.017          1.378         3.863\n",
        "\t\n",
        "\n",
        "std_('F', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.115            0.018            0.001            [ 0.082  0.152]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.087            0.102           0.113          0.125         0.16\n",
        "\t\n",
        "\n",
        "msd_val_sim_('E', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.066           0.603            0.005            [-3.022 -1.163]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.974           -2.308          -2.073         -1.835        -1.111\n",
        "\t\n",
        "\n",
        "outlier_sim_('F', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.24             0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('A', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.065            0.247            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "msd_val_sim_('A', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.928           0.828            0.008            [-4.464 -1.425]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.45            -3.386          -2.931         -2.467        -1.401\n",
        "\t\n",
        "\n",
        "msd_val_sim_('E', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.401            0.52             0.004            [-0.16   0.931]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.14            0.286           0.402          0.518         0.968\n",
        "\t\n",
        "\n",
        "outlier_sim_('F', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.065            0.247            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "std_('C', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.829            0.114            0.002            [ 0.616  1.05 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.636            0.749           0.818          0.899         1.082\n",
        "\t\n",
        "\n",
        "outlier_('A', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.028            0.166            0.006                  [ 0.  0.]\n",
        "\t0.024            0.151            0.006                  [ 0.  0.]\n",
        "\t0.035            0.185            0.007                  [ 0.  0.]\n",
        "\t0.036            0.186            0.007                  [ 0.  0.]\n",
        "\t0.106            0.307            0.013                  [ 0.  1.]\n",
        "\t0.018            0.131            0.004                  [ 0.  0.]\n",
        "\t0.021            0.142            0.005                  [ 0.  0.]\n",
        "\t0.068            0.252            0.01                   [ 0.  1.]\n",
        "\t0.087            0.282            0.013                  [ 0.  1.]\n",
        "\t0.051            0.22             0.009                  [ 0.  1.]\n",
        "\t0.07             0.256            0.011                  [ 0.  1.]\n",
        "\t0.036            0.188            0.006                  [ 0.  0.]\n",
        "\t0.079            0.27             0.012                  [ 0.  1.]\n",
        "\t0.025            0.157            0.006                  [ 0.  0.]\n",
        "\t0.022            0.147            0.006                  [ 0.  0.]\n",
        "\t0.019            0.138            0.005                  [ 0.  0.]\n",
        "\t0.035            0.183            0.007                  [ 0.  0.]\n",
        "\t0.044            0.206            0.009                  [ 0.  0.]\n",
        "\t0.055            0.228            0.011                  [ 0.  1.]\n",
        "\t0.022            0.147            0.005                  [ 0.  0.]\n",
        "\t0.019            0.138            0.005                  [ 0.  0.]\n",
        "\t0.035            0.183            0.007                  [ 0.  0.]\n",
        "\t0.047            0.211            0.009                  [ 0.  0.]\n",
        "\t0.048            0.214            0.009                  [ 0.  0.]\n",
        "\t0.037            0.188            0.01                   [ 0.  0.]\n",
        "\t0.018            0.132            0.005                  [ 0.  0.]\n",
        "\t0.027            0.161            0.006                  [ 0.  0.]\n",
        "\t0.018            0.132            0.004                  [ 0.  0.]\n",
        "\t0.015            0.12             0.004                  [ 0.  0.]\n",
        "\t0.129            0.335            0.015                  [ 0.  1.]\n",
        "\t0.013            0.114            0.004                  [ 0.  0.]\n",
        "\t0.029            0.167            0.007                  [ 0.  0.]\n",
        "\t0.029            0.167            0.006                  [ 0.  0.]\n",
        "\t0.041            0.197            0.007                  [ 0.  0.]\n",
        "\t0.026            0.159            0.006                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "true_val_('C', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.373           0.076            0.002            [-2.518 -2.219]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.517           -2.424          -2.375         -2.324        -2.218\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('D', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.014           1.982            0.017            [-3.811  3.931]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.883           -1.356          -0.012         1.332         3.864\n",
        "\t\n",
        "\n",
        "outlier_('C', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.024            0.153            0.006                  [ 0.  0.]\n",
        "\t0.02             0.139            0.005                  [ 0.  0.]\n",
        "\t0.011            0.105            0.004                  [ 0.  0.]\n",
        "\t0.026            0.158            0.007                  [ 0.  0.]\n",
        "\t0.035            0.184            0.008                  [ 0.  0.]\n",
        "\t0.011            0.105            0.004                  [ 0.  0.]\n",
        "\t0.677            0.468            0.032                  [ 0.  1.]\n",
        "\t0.014            0.116            0.005                  [ 0.  0.]\n",
        "\t0.091            0.288            0.015                  [ 0.  1.]\n",
        "\t0.016            0.124            0.005                  [ 0.  0.]\n",
        "\t0.027            0.163            0.008                  [ 0.  0.]\n",
        "\t0.052            0.222            0.012                  [ 0.  1.]\n",
        "\t0.025            0.157            0.006                  [ 0.  0.]\n",
        "\t0.012            0.11             0.004                  [ 0.  0.]\n",
        "\t0.024            0.153            0.007                  [ 0.  0.]\n",
        "\t0.661            0.473            0.039                  [ 0.  1.]\n",
        "\t0.12             0.325            0.019                  [ 0.  1.]\n",
        "\t0.281            0.45             0.029                  [ 0.  1.]\n",
        "\t0.725            0.446            0.029                  [ 0.  1.]\n",
        "\t0.02             0.14             0.006                  [ 0.  0.]\n",
        "\t0.019            0.136            0.006                  [ 0.  0.]\n",
        "\t0.012            0.109            0.004                  [ 0.  0.]\n",
        "\t0.027            0.162            0.007                  [ 0.  0.]\n",
        "\t0.127            0.333            0.019                  [ 0.  1.]\n",
        "\t0.021            0.143            0.006                  [ 0.  0.]\n",
        "\t0.014            0.118            0.005                  [ 0.  0.]\n",
        "\t0.023            0.15             0.005                  [ 0.  0.]\n",
        "\t0.029            0.167            0.006                  [ 0.  0.]\n",
        "\t0.012            0.107            0.004                  [ 0.  0.]\n",
        "\t0.01             0.098            0.004                  [ 0.  0.]\n",
        "\t0.005            0.074            0.003                  [ 0.  0.]\n",
        "\t0.015            0.123            0.005                  [ 0.  0.]\n",
        "\t0.013            0.114            0.005                  [ 0.  0.]\n",
        "\t0.028            0.166            0.007                  [ 0.  0.]\n",
        "\t0.419            0.493            0.034                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            1.0           1.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            1.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('F', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.448            0.2              0.02             [ 0.164  0.906]\n",
        "\t1.608            2.19             0.217            [-1.775  5.817]\n",
        "\t1.222            1.766            0.174            [-1.951  4.126]\n",
        "\t0.164            1.036            0.101            [-1.859  2.086]\n",
        "\t0.406            0.063            0.001            [ 0.275  0.521]\n",
        "\t-0.155           2.338            0.23             [-3.819  4.49 ]\n",
        "\t-0.136           1.72             0.154            [-3.294  3.079]\n",
        "\t-0.279           1.928            0.158            [-3.782  3.747]\n",
        "\t-1.213           0.065            0.001            [-1.338 -1.081]\n",
        "\t0.432            1.641            0.161            [-2.906  3.326]\n",
        "\t-0.74            0.613            0.061            [-1.796  0.518]\n",
        "\t-0.552           1.823            0.175            [-3.98   2.649]\n",
        "\t1.572            1.927            0.19             [-2.189  5.408]\n",
        "\t0.309            0.412            0.041            [-0.63   0.945]\n",
        "\t-0.632           2.314            0.228            [-4.315  3.517]\n",
        "\t0.811            2.014            0.185            [-3.217  4.476]\n",
        "\t0.831            0.413            0.041            [ 0.1    1.656]\n",
        "\t-0.218           2.295            0.226            [-4.348  4.558]\n",
        "\t0.369            1.899            0.178            [-3.284  3.619]\n",
        "\t0.24             2.347            0.233            [-4.018  4.809]\n",
        "\t-1.359           1.76             0.171            [-4.458  2.353]\n",
        "\t1.297            1.004            0.1              [ 0.069  3.801]\n",
        "\t0.415            1.788            0.166            [-3.418  3.701]\n",
        "\t0.695            2.482            0.246            [-3.265  5.413]\n",
        "\t2.049            1.088            0.109            [ 0.365  3.562]\n",
        "\t-2.23            0.064            0.001            [-2.354 -2.103]\n",
        "\t-0.891           2.09             0.206            [-5.195  2.892]\n",
        "\t-0.029           1.629            0.154            [-2.913  3.025]\n",
        "\t-0.262           1.761            0.171            [-3.563  2.648]\n",
        "\t0.124            1.549            0.144            [-3.243  3.142]\n",
        "\t0.441            1.905            0.187            [-3.885  3.477]\n",
        "\t-1.826           1.483            0.148            [-4.157  0.409]\n",
        "\t-0.326           1.367            0.135            [-3.481  1.902]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.167            0.302           0.394          0.544         0.911\n",
        "\t-1.945           -0.322          1.493          3.554         5.679\n",
        "\t-1.914           -0.311          1.495          2.538         4.178\n",
        "\t-1.989           -0.557          0.206          0.917         2.01\n",
        "\t0.283            0.364           0.406          0.446         0.53\n",
        "\t-3.91            -2.036          -0.432         1.461         4.438\n",
        "\t-3.304           -1.351          -0.181         1.154         3.078\n",
        "\t-4.063           -1.576          -0.387         0.996         3.589\n",
        "\t-1.341           -1.255          -1.213         -1.171        -1.084\n",
        "\t-2.919           -0.302          0.592          1.421         3.318\n",
        "\t-1.757           -1.181          -0.762         -0.399        0.608\n",
        "\t-3.89            -1.897          -0.49          0.691         2.85\n",
        "\t-2.245           0.31            1.402          2.755         5.372\n",
        "\t-0.628           0.089           0.38           0.578         0.95\n",
        "\t-3.732           -2.502          -1.24          1.098         4.98\n",
        "\t-3.018           -0.509          0.88           2.157         4.76\n",
        "\t0.11             0.512           0.833          1.117         1.67\n",
        "\t-5.071           -1.825          -0.324         1.431         4.097\n",
        "\t-2.994           -1.164          0.336          1.812         4.062\n",
        "\t-4.177           -1.602          0.411          1.812         4.687\n",
        "\t-5.123           -2.581          -1.41          -0.03         1.902\n",
        "\t-0.523           0.566           0.986          2.011         3.56\n",
        "\t-3.209           -0.758          0.413          1.638         3.954\n",
        "\t-3.543           -1.287          0.498          2.492         5.25\n",
        "\t0.056            0.833           2.471          2.966         3.404\n",
        "\t-2.358           -2.272          -2.229         -2.188        -2.107\n",
        "\t-5.109           -2.367          -0.854         0.622         3.006\n",
        "\t-2.919           -1.292          -0.071         1.158         3.025\n",
        "\t-3.411           -1.743          -0.161         1.204         2.831\n",
        "\t-3.566           -0.663          0.21           1.126         2.996\n",
        "\t-3.768           -0.911          0.714          1.836         3.635\n",
        "\t-4.103           -3.222          -1.589         -0.549        0.468\n",
        "\t-3.301           -1.112          -0.353         0.649         2.17\n",
        "\t\n",
        "\n",
        "true_val_('A', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.802           0.139            0.002            [-3.065 -2.521]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.078           -2.893          -2.803         -2.71         -2.53\n",
        "\t\n",
        "\n",
        "msd_val_sim_('A', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.804           0.924            0.008            [-4.523 -1.021]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.53            -3.356          -2.806         -2.266        -1.024\n",
        "\t\n",
        "\n",
        "outlier_sim_('E', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.062            0.241            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('E', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.064            0.245            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "true_val_('E', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.054            0.022            0.001            [ 0.009  0.097]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.008            0.04            0.055          0.069         0.096\n",
        "\t\n",
        "\n",
        "true_val_('E', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.401            0.029            0.0              [ 0.344  0.457]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.345            0.382           0.401          0.42          0.458\n",
        "\t\n",
        "\n",
        "outlier_dist_('E', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.099           1.976            0.152            [-4.114  3.536]\n",
        "\t0.398            2.013            0.158            [-3.374  4.309]\n",
        "\t-0.092           2.008            0.193            [-5.124  3.152]\n",
        "\t0.096            2.042            0.09             [-3.792  4.267]\n",
        "\t0.005            1.995            0.092            [-3.895  3.803]\n",
        "\t-0.032           1.92             0.08             [-3.887  3.737]\n",
        "\t-0.053           2.011            0.106            [-3.989  3.861]\n",
        "\t0.519            1.74             0.159            [-2.95   3.605]\n",
        "\t-0.042           2.029            0.094            [-4.132  3.778]\n",
        "\t-0.107           2.041            0.087            [-4.119  3.938]\n",
        "\t-0.224           1.924            0.095            [-3.698  3.731]\n",
        "\t-0.127           2.107            0.143            [-4.23   4.143]\n",
        "\t-0.069           2.024            0.101            [-4.054  4.047]\n",
        "\t0.131            1.976            0.137            [-3.738  3.797]\n",
        "\t-0.165           1.955            0.093            [-3.989  3.734]\n",
        "\t0.0              1.965            0.106            [-3.736  3.776]\n",
        "\t-0.09            1.956            0.072            [-3.93   3.773]\n",
        "\t0.142            1.933            0.178            [-3.26   3.652]\n",
        "\t-1.265           1.277            0.124            [-4.101  0.936]\n",
        "\t0.483            1.915            0.141            [-3.206  4.514]\n",
        "\t-0.043           2.176            0.157            [-4.724  3.951]\n",
        "\t0.067            1.97             0.086            [-3.743  3.993]\n",
        "\t0.015            1.967            0.12             [-3.754  3.936]\n",
        "\t-0.349           1.724            0.132            [-4.119  3.207]\n",
        "\t-0.343           1.241            0.121            [-3.389  2.09 ]\n",
        "\t-0.007           2.036            0.051            [-4.01   3.886]\n",
        "\t-0.154           2.198            0.146            [-4.254  4.153]\n",
        "\t0.036            1.77             0.139            [-3.403  3.492]\n",
        "\t0.052            1.152            0.113            [-2.223  2.068]\n",
        "\t0.06             1.968            0.067            [-3.974  3.767]\n",
        "\t0.04             1.915            0.098            [-3.554  3.77 ]\n",
        "\t0.087            1.981            0.091            [-3.977  3.93 ]\n",
        "\t0.202            2.014            0.098            [-3.688  4.212]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.967           -1.458          -0.04          1.251         3.761\n",
        "\t-3.393           -1.001          0.435          1.753         4.297\n",
        "\t-4.761           -1.134          0.065          1.265         3.62\n",
        "\t-4.125           -1.241          0.133          1.512         3.992\n",
        "\t-3.915           -1.391          0.036          1.404         3.797\n",
        "\t-3.829           -1.299          0.107          1.15          3.832\n",
        "\t-3.966           -1.398          -0.052         1.267         3.955\n",
        "\t-2.919           -0.595          0.53           1.673         3.659\n",
        "\t-3.944           -1.428          -0.078         1.31          4.083\n",
        "\t-4.056           -1.481          -0.117         1.23          4.012\n",
        "\t-3.819           -1.518          -0.305         1.06          3.653\n",
        "\t-4.358           -1.539          -0.132         1.299         4.057\n",
        "\t-4.129           -1.386          -0.084         1.266         4.014\n",
        "\t-3.459           -1.279          0.059          1.431         4.233\n",
        "\t-3.984           -1.455          -0.155         1.113         3.747\n",
        "\t-3.718           -1.344          0.009          1.359         3.798\n",
        "\t-3.795           -1.432          -0.162         1.187         3.926\n",
        "\t-3.327           -1.356          0.138          1.672         3.613\n",
        "\t-4.056           -2.123          -1.24          -0.344        1.011\n",
        "\t-3.321           -0.794          0.37           1.719         4.442\n",
        "\t-4.694           -1.345          0.019          1.434         3.998\n",
        "\t-3.843           -1.197          0.043          1.35          3.91\n",
        "\t-4.0             -1.269          0.037          1.308         3.783\n",
        "\t-3.974           -1.224          -0.331         0.356         3.397\n",
        "\t-3.218           -0.539          -0.408         -0.111        2.335\n",
        "\t-3.997           -1.38           0.004          1.349         3.925\n",
        "\t-4.254           -1.692          -0.152         1.363         4.158\n",
        "\t-3.48            -1.143          0.027          1.255         3.442\n",
        "\t-2.548           -0.597          0.173          0.917         1.965\n",
        "\t-3.843           -1.252          0.051          1.393         3.941\n",
        "\t-3.651           -1.263          0.065          1.364         3.704\n",
        "\t-4.031           -1.215          0.145          1.43          3.904\n",
        "\t-3.74            -1.136          0.202          1.54          4.198\n",
        "\t\n",
        "\n",
        "outlier_dist_('A', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.036            2.016            0.076            [-4.161  3.789]\n",
        "\t0.069            2.065            0.125            [-3.772  4.178]\n",
        "\t0.029            1.954            0.058            [-3.776  3.987]\n",
        "\t-0.235           1.156            0.115            [-1.81   1.731]\n",
        "\t0.223            1.897            0.059            [-3.61   3.802]\n",
        "\t-0.024           1.927            0.041            [-3.723  3.845]\n",
        "\t-1.207           1.279            0.127            [-3.369  1.009]\n",
        "\t-0.131           1.801            0.165            [-3.707  3.625]\n",
        "\t-0.24            1.949            0.074              [-4.21  3.7 ]\n",
        "\t-0.152           2.001            0.162            [-3.886  3.926]\n",
        "\t-0.283           1.911            0.163            [-3.709  3.492]\n",
        "\t0.413            1.937            0.124            [-3.473  4.081]\n",
        "\t-0.156           1.932            0.031              [-3.9   3.67]\n",
        "\t0.052            1.79             0.099            [-3.317  3.583]\n",
        "\t-0.09            2.008            0.114            [-3.943  3.984]\n",
        "\t-0.042           1.94             0.138            [-3.865  3.966]\n",
        "\t0.519            1.814            0.162            [-3.07   3.762]\n",
        "\t0.003            1.873            0.1              [-3.753  3.654]\n",
        "\t-0.829           2.399            0.235            [-4.862  3.929]\n",
        "\t0.003            1.97             0.076            [-3.678  4.019]\n",
        "\t0.006            2.003            0.036            [-3.985  3.888]\n",
        "\t-0.027           1.926            0.156            [-4.055  3.678]\n",
        "\t0.054            1.951            0.076            [-3.546  4.071]\n",
        "\t0.015            1.894            0.076            [-3.813  3.69 ]\n",
        "\t0.142            2.233            0.199            [-4.159  4.51 ]\n",
        "\t0.256            1.921            0.129            [-3.351  4.227]\n",
        "\t0.516            1.952            0.181            [-2.809  3.99 ]\n",
        "\t-1.282           1.817            0.176            [-4.368  2.352]\n",
        "\t-0.345           1.944            0.189            [-3.512  2.9  ]\n",
        "\t0.122            1.955            0.093            [-3.964  3.819]\n",
        "\t-0.812           1.966            0.186            [-4.585  2.983]\n",
        "\t0.274            2.051            0.156            [-3.592  4.428]\n",
        "\t-0.042           1.963            0.041            [-3.913  3.751]\n",
        "\t0.154            1.996            0.141            [-3.423  4.569]\n",
        "\t-0.013           1.997            0.067            [-3.858  4.111]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.906           -1.285          0.064          1.356         4.056\n",
        "\t-3.773           -1.38           0.042          1.437         4.177\n",
        "\t-3.856           -1.288          0.045          1.323         3.931\n",
        "\t-2.025           -1.089          -0.698         1.065         1.678\n",
        "\t-3.608           -1.038          0.329          1.506         3.826\n",
        "\t-3.774           -1.273          -0.07          1.245         3.816\n",
        "\t-3.176           -2.194          -1.464         -0.122        1.516\n",
        "\t-3.86            -1.196          -0.2           1.003         3.508\n",
        "\t-4.063           -1.497          -0.325         0.997         3.869\n",
        "\t-4.006           -1.453          -0.247         1.186         3.85\n",
        "\t-3.701           -1.616          -0.351         0.983         3.504\n",
        "\t-3.458           -0.915          0.382          1.76          4.104\n",
        "\t-3.85            -1.445          -0.239         1.128         3.734\n",
        "\t-3.469           -1.171          0.087          1.268         3.448\n",
        "\t-4.165           -1.429          -0.083         1.271         3.813\n",
        "\t-3.858           -1.238          -0.045         1.154         4.004\n",
        "\t-2.919           -0.782          0.535          1.821         3.958\n",
        "\t-3.641           -1.267          -0.077         1.234         3.799\n",
        "\t-5.504           -2.539          -0.956         1.122         3.545\n",
        "\t-3.9             -1.297          -0.004         1.343         3.896\n",
        "\t-3.932           -1.309          -0.005         1.354         3.955\n",
        "\t-3.862           -1.285          -0.046         1.139         3.925\n",
        "\t-3.715           -1.251          0.04           1.354         3.931\n",
        "\t-3.789           -1.218          0.043          1.25          3.728\n",
        "\t-4.729           -1.353          0.324          1.624         4.089\n",
        "\t-3.558           -1.004          0.265          1.495         4.03\n",
        "\t-2.796           -1.073          0.48           2.006         4.018\n",
        "\t-4.62            -2.714          -1.131         -0.075        2.21\n",
        "\t-3.366           -2.012          -0.684         1.336         3.479\n",
        "\t-3.964           -1.125          0.246          1.439         3.824\n",
        "\t-4.974           -2.124          -0.574         0.513         2.739\n",
        "\t-3.797           -1.103          0.258          1.655         4.277\n",
        "\t-3.888           -1.349          -0.06          1.275         3.79\n",
        "\t-3.625           -1.181          0.008          1.472         4.413\n",
        "\t-4.03            -1.316          -0.02          1.325         3.983\n",
        "\t\n",
        "\n",
        "outlier_dist_('D', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.01             1.959            0.089            [-4.08   3.648]\n",
        "\t0.083            1.82             0.166            [-3.133  3.701]\n",
        "\t0.052            2.037            0.063            [-4.016  3.947]\n",
        "\t0.019            2.026            0.086            [-4.134  3.666]\n",
        "\t-0.011           1.923            0.114            [-3.636  3.694]\n",
        "\t-0.0             1.937            0.109            [-3.828  3.782]\n",
        "\t-0.032           1.974            0.172            [-3.978  3.718]\n",
        "\t-0.125           1.968            0.077            [-3.973  3.672]\n",
        "\t0.063            1.962            0.074            [-3.599  3.989]\n",
        "\t0.285            1.823            0.153            [-3.175  3.95 ]\n",
        "\t-0.001           1.971            0.039            [-3.822  3.856]\n",
        "\t0.075            2.054            0.062            [-4.018  3.896]\n",
        "\t-0.181           2.091            0.095            [-4.29   4.005]\n",
        "\t-0.087           1.968            0.102            [-3.785  3.772]\n",
        "\t0.104            2.048            0.118            [-3.811  4.22 ]\n",
        "\t0.242            1.944            0.172            [-3.436  4.162]\n",
        "\t0.156            1.885            0.149            [-3.412  4.113]\n",
        "\t0.044            2.045            0.134            [-4.025  3.933]\n",
        "\t0.118            2.05             0.18             [-3.58   4.495]\n",
        "\t-0.762           1.364            0.135            [-3.259  1.651]\n",
        "\t0.272            1.466            0.141            [-3.832  3.057]\n",
        "\t-0.074           1.903            0.06             [-3.794  3.761]\n",
        "\t-0.039           2.002            0.091            [-4.078  3.791]\n",
        "\t-0.398           1.217            0.117            [-3.01   1.571]\n",
        "\t-0.056           1.823            0.16             [-3.489  3.305]\n",
        "\t-0.007           2.025            0.085            [-3.798  4.314]\n",
        "\t0.172            2.069            0.184            [-3.974  4.046]\n",
        "\t-0.303           1.981            0.164            [-3.822  3.923]\n",
        "\t-0.057           1.926            0.135            [-3.723  3.949]\n",
        "\t0.191            2.08             0.139            [-3.823  4.467]\n",
        "\t-0.239           1.994            0.163            [-4.414  3.517]\n",
        "\t0.028            1.981            0.086            [-3.712  3.83 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.05            -1.278          0.07           1.384         3.699\n",
        "\t-3.644           -1.166          -0.022         1.484         3.443\n",
        "\t-3.936           -1.33           0.075          1.401         4.08\n",
        "\t-3.98            -1.356          0.035          1.424         3.841\n",
        "\t-3.625           -1.379          -0.067         1.301         3.738\n",
        "\t-3.87            -1.304          0.029          1.303         3.75\n",
        "\t-3.756           -1.362          -0.099         1.288         4.181\n",
        "\t-4.081           -1.411          -0.114         1.254         3.623\n",
        "\t-3.573           -1.316          0.004          1.38          4.069\n",
        "\t-3.366           -0.956          0.295          1.531         3.803\n",
        "\t-3.919           -1.302          -0.011         1.321         3.807\n",
        "\t-3.831           -1.335          0.076          1.477         4.116\n",
        "\t-4.302           -1.54           -0.164         1.22          4.005\n",
        "\t-3.729           -1.462          -0.117         1.27          3.863\n",
        "\t-4.136           -1.202          0.106          1.466         3.998\n",
        "\t-3.575           -1.05           0.298          1.515         4.07\n",
        "\t-3.376           -1.104          0.034          1.318         4.155\n",
        "\t-4.025           -1.334          0.099          1.5           3.933\n",
        "\t-3.632           -1.349          0.02           1.371         4.458\n",
        "\t-3.446           -1.606          -0.86          0.351         1.547\n",
        "\t-4.697           0.267           0.383          0.532         2.722\n",
        "\t-3.801           -1.278          -0.2           1.155         3.757\n",
        "\t-3.839           -1.402          -0.095         1.233         4.131\n",
        "\t-3.104           -1.089          -0.208         0.47          1.504\n",
        "\t-3.516           -1.397          -0.003         1.254         3.291\n",
        "\t-3.984           -1.386          -0.004         1.3           4.173\n",
        "\t-4.265           -1.111          0.194          1.607         3.9\n",
        "\t-4.147           -1.663          -0.416         1.064         3.652\n",
        "\t-4.162           -1.179          -0.184         1.185         3.718\n",
        "\t-3.809           -1.232          0.132          1.517         4.504\n",
        "\t-4.146           -1.504          -0.228         1.044         3.941\n",
        "\t-3.823           -1.305          0.019          1.367         3.746\n",
        "\t\n",
        "\n",
        "std_('A', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.644            0.098            0.004            [ 0.467  0.834]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.484            0.574           0.633          0.703         0.863\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('E', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.013           2.005            0.017            [-3.923  3.889]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.992           -1.378          -0.011         1.355         3.845\n",
        "\t\n",
        "\n",
        "outlier_sim_('C', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.061            0.24             0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "std_('E', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.16             0.021            0.0              [ 0.12   0.201]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.124            0.145           0.157          0.172         0.207\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "true_val_('F', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.015            0.011            0.0              [-0.007  0.038]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.007           0.008           0.015          0.023         0.038\n",
        "\t\n",
        "\n",
        "outlier_dist_('E', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.103           2.098            0.126            [-4.339  3.989]\n",
        "\t0.009            2.047            0.075            [-3.931  4.117]\n",
        "\t0.025            1.984            0.07             [-3.691  4.004]\n",
        "\t-0.029           1.97             0.089            [-3.672  4.134]\n",
        "\t-0.006           1.929            0.057            [-3.641  3.829]\n",
        "\t-0.075           1.895            0.104            [-3.88   3.362]\n",
        "\t-0.15            2.009            0.131            [-4.195  3.601]\n",
        "\t-0.102           1.989            0.08             [-3.862  3.989]\n",
        "\t0.051            2.119            0.171            [-4.016  3.93 ]\n",
        "\t0.044            1.973            0.07             [-3.856  3.828]\n",
        "\t-0.322           1.92             0.15             [-4.179  3.262]\n",
        "\t-0.097           1.954            0.093            [-3.752  3.902]\n",
        "\t-0.276           2.07             0.129            [-4.387  3.539]\n",
        "\t0.005            2.047            0.114            [-3.898  4.045]\n",
        "\t-0.059           1.981            0.062            [-3.895  3.798]\n",
        "\t0.109            1.885            0.117            [-4.011  3.631]\n",
        "\t0.351            0.063            0.006            [ 0.255  0.487]\n",
        "\t-0.04            1.933            0.055            [-4.016  3.618]\n",
        "\t0.566            2.069            0.19             [-3.54   4.754]\n",
        "\t-0.586           1.886            0.173            [-4.52   2.721]\n",
        "\t0.075            2.056            0.118            [-3.747  4.389]\n",
        "\t0.495            1.923            0.172            [-3.093  3.898]\n",
        "\t0.264            1.837            0.164            [-3.256  3.684]\n",
        "\t0.203            1.782            0.176            [-3.208  3.245]\n",
        "\t-0.141           1.965            0.077            [-4.071  3.812]\n",
        "\t-0.003           1.965            0.039            [-3.672  3.93 ]\n",
        "\t0.193            2.07             0.162            [-3.604  4.17 ]\n",
        "\t0.014            1.986            0.057            [-3.913  3.939]\n",
        "\t0.141            1.969            0.135            [-3.502  4.288]\n",
        "\t-0.464           2.058            0.197            [-4.525  3.422]\n",
        "\t0.067            1.971            0.04             [-3.755  4.001]\n",
        "\t0.007            1.933            0.07             [-3.819  3.653]\n",
        "\t0.102            1.894            0.089            [-3.504  3.928]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-4.303           -1.483          -0.103         1.301         4.047\n",
        "\t-4.031           -1.368          0.006          1.361         4.057\n",
        "\t-3.852           -1.316          0.048          1.337         3.911\n",
        "\t-3.875           -1.353          -0.051         1.249         3.962\n",
        "\t-3.704           -1.331          -0.017         1.304         3.781\n",
        "\t-3.826           -1.335          -0.077         1.268         3.449\n",
        "\t-4.136           -1.486          -0.114         1.249         3.677\n",
        "\t-3.941           -1.441          -0.123         1.194         3.915\n",
        "\t-3.822           -1.39           -0.035         1.408         4.249\n",
        "\t-3.764           -1.311          0.037          1.366         3.972\n",
        "\t-4.304           -1.397          -0.239         0.928         3.262\n",
        "\t-3.925           -1.369          -0.066         1.186         3.768\n",
        "\t-4.146           -1.725          -0.326         1.1           3.857\n",
        "\t-4.107           -1.341          0.029          1.428         3.923\n",
        "\t-3.987           -1.373          -0.072         1.297         3.756\n",
        "\t-3.685           -1.114          0.123          1.328         3.991\n",
        "\t0.242            0.306           0.35           0.38          0.481\n",
        "\t-3.868           -1.342          -0.037         1.242         3.804\n",
        "\t-3.75            -0.763          0.532          1.841         4.645\n",
        "\t-4.362           -1.857          -0.579         0.711         2.902\n",
        "\t-4.057           -1.244          0.034          1.452         4.123\n",
        "\t-2.98            -0.913          0.443          1.877         4.052\n",
        "\t-3.358           -0.969          0.357          1.52          3.614\n",
        "\t-3.013           -1.105          0.187          1.568         3.53\n",
        "\t-4.027           -1.454          -0.161         1.125         3.873\n",
        "\t-3.814           -1.301          -0.01          1.326         3.831\n",
        "\t-3.555           -1.331          0.146          1.635         4.233\n",
        "\t-3.95            -1.287          0.04           1.351         3.914\n",
        "\t-3.909           -1.239          0.195          1.505         3.943\n",
        "\t-4.472           -1.861          -0.299         0.949         3.502\n",
        "\t-3.801           -1.251          0.039          1.386         3.984\n",
        "\t-3.775           -1.325          0.057          1.322         3.73\n",
        "\t-3.579           -1.178          0.077          1.351         3.869\n",
        "\t\n",
        "\n",
        "outlier_dist_('B', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.135            1.947            0.124            [-3.714  3.875]\n",
        "\t-0.31            2.021            0.169            [-4.481  3.49 ]\n",
        "\t0.111            1.931            0.078            [-3.597  3.817]\n",
        "\t0.209            1.833            0.168            [-3.447  3.4  ]\n",
        "\t-0.501           1.969            0.184            [-4.495  3.598]\n",
        "\t3.338            2.142            0.213            [-0.019  7.455]\n",
        "\t0.057            2.032            0.135            [-3.918  3.999]\n",
        "\t0.514            1.61             0.132            [-2.739  3.444]\n",
        "\t1.65             0.878            0.083            [ 0.151  3.406]\n",
        "\t-0.017           1.989            0.17             [-4.08   3.696]\n",
        "\t0.076            1.988            0.12             [-3.765  4.028]\n",
        "\t0.206            1.232            0.122            [-2.46   2.295]\n",
        "\t0.02             1.506            0.083            [-3.521  3.22 ]\n",
        "\t0.088            1.882            0.169            [-3.589  3.763]\n",
        "\t-0.12            1.861            0.137            [-3.786  3.607]\n",
        "\t0.415            1.772            0.135            [-2.906  4.051]\n",
        "\t-0.117           1.928            0.094            [-3.875  3.86 ]\n",
        "\t0.027            1.973            0.1              [-4.052  3.634]\n",
        "\t0.285            2.079            0.113            [-4.032  4.275]\n",
        "\t-0.007           1.871            0.112            [-3.338  3.852]\n",
        "\t0.015            1.975            0.073            [-4.049  3.716]\n",
        "\t1.186            1.109            0.109            [-0.959  3.42 ]\n",
        "\t0.08             2.088            0.133              [-3.87  4.25]\n",
        "\t0.039            1.956            0.149            [-3.526  4.155]\n",
        "\t0.087            1.955            0.105            [-3.769  3.84 ]\n",
        "\t-0.011           2.018            0.1              [-3.95   4.156]\n",
        "\t0.452            1.847            0.151            [-3.104  3.945]\n",
        "\t-0.207           1.972            0.066            [-3.926  3.704]\n",
        "\t-0.404           1.909            0.132            [-4.087  3.294]\n",
        "\t-0.0             1.944            0.101            [-3.813  3.796]\n",
        "\t0.049            1.979            0.064            [-3.969  3.749]\n",
        "\t-0.509           1.733            0.155            [-3.726  2.571]\n",
        "\t0.058            2.393            0.229            [-4.919  3.858]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.75            -1.136          0.167          1.467         3.85\n",
        "\t-4.492           -1.537          -0.257         1.026         3.484\n",
        "\t-3.528           -1.203          0.055          1.398         3.995\n",
        "\t-3.776           -0.945          0.389          1.574         3.236\n",
        "\t-4.167           -1.829          -0.606         0.605         4.185\n",
        "\t0.162            1.769           3.138          4.569         7.758\n",
        "\t-3.846           -1.286          -0.017         1.405         4.087\n",
        "\t-2.748           -0.544          0.579          1.627         3.441\n",
        "\t0.213            0.973           1.57           2.236         3.522\n",
        "\t-3.951           -1.353          0.038          1.235         3.887\n",
        "\t-3.978           -1.253          0.072          1.446         3.898\n",
        "\t-2.352           -0.323          0.433          0.935         2.621\n",
        "\t-3.475           -0.388          0.26           0.371         3.321\n",
        "\t-3.621           -1.234          0.204          1.264         3.748\n",
        "\t-4.177           -1.297          -0.141         1.179         3.368\n",
        "\t-2.876           -0.793          0.27           1.617         4.098\n",
        "\t-3.966           -1.39           -0.103         1.132         3.798\n",
        "\t-3.915           -1.29           0.048          1.344         3.809\n",
        "\t-3.894           -1.076          0.24           1.709         4.493\n",
        "\t-3.546           -1.295          -0.056         1.256         3.729\n",
        "\t-3.979           -1.28           0.026          1.349         3.839\n",
        "\t-0.951           0.481           1.107          1.735         3.442\n",
        "\t-4.033           -1.328          0.082          1.524         4.132\n",
        "\t-3.886           -1.209          0.049          1.372         3.88\n",
        "\t-3.857           -1.209          0.078          1.429         3.806\n",
        "\t-4.303           -1.282          0.08           1.283         3.839\n",
        "\t-3.06            -0.885          0.313          1.8           4.013\n",
        "\t-4.083           -1.539          -0.171         1.138         3.633\n",
        "\t-4.092           -1.748          -0.4           0.945         3.294\n",
        "\t-3.769           -1.355          -0.0           1.291         3.868\n",
        "\t-3.9             -1.269          0.033          1.403         3.878\n",
        "\t-3.863           -1.812          -0.513         0.869         2.492\n",
        "\t-5.067           -1.515          0.358          1.801         3.776\n",
        "\t\n",
        "\n",
        "std_('D', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.086            0.015            0.001            [ 0.059  0.116]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.063            0.076           0.084          0.094         0.122\n",
        "\t\n",
        "\n",
        "msd_val_sim_('F', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.015            0.504            0.004            [-0.655  0.273]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.456           -0.03           0.016          0.061         0.523\n",
        "\t\n",
        "\n",
        "outlier_sim_('C', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.063            0.243            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "std_('E', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.329            0.051            0.001            [ 0.235  0.43 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.243            0.294           0.324          0.358         0.444\n",
        "\t\n",
        "\n",
        "outlier_('A', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.042            0.202            0.008                  [ 0.  0.]\n",
        "\t0.02             0.14             0.004                  [ 0.  0.]\n",
        "\t0.032            0.177            0.006                  [ 0.  0.]\n",
        "\t0.048            0.213            0.007                  [ 0.  0.]\n",
        "\t0.159            0.366            0.015                  [ 0.  1.]\n",
        "\t0.158            0.365            0.019                  [ 0.  1.]\n",
        "\t0.036            0.187            0.007                  [ 0.  0.]\n",
        "\t0.056            0.229            0.009                  [ 0.  1.]\n",
        "\t0.045            0.207            0.008                  [ 0.  0.]\n",
        "\t0.034            0.182            0.006                  [ 0.  0.]\n",
        "\t0.023            0.15             0.005                  [ 0.  0.]\n",
        "\t0.029            0.169            0.007                  [ 0.  0.]\n",
        "\t0.022            0.146            0.005                  [ 0.  0.]\n",
        "\t0.031            0.172            0.006                  [ 0.  0.]\n",
        "\t0.124            0.329            0.017                  [ 0.  1.]\n",
        "\t0.053            0.225            0.008                  [ 0.  1.]\n",
        "\t0.077            0.267            0.011                  [ 0.  1.]\n",
        "\t0.035            0.183            0.007                  [ 0.  0.]\n",
        "\t0.061            0.239            0.009                  [ 0.  1.]\n",
        "\t0.024            0.152            0.005                  [ 0.  0.]\n",
        "\t0.035            0.184            0.008                  [ 0.  0.]\n",
        "\t0.026            0.159            0.006                  [ 0.  0.]\n",
        "\t0.033            0.177            0.006                  [ 0.  0.]\n",
        "\t0.049            0.216            0.007                  [ 0.  0.]\n",
        "\t0.056            0.231            0.009                  [ 0.  1.]\n",
        "\t0.027            0.163            0.007                  [ 0.  0.]\n",
        "\t0.046            0.209            0.009                  [ 0.  0.]\n",
        "\t0.046            0.209            0.009                  [ 0.  0.]\n",
        "\t0.04             0.196            0.007                  [ 0.  0.]\n",
        "\t0.123            0.328            0.015                  [ 0.  1.]\n",
        "\t0.03             0.17             0.007                  [ 0.  0.]\n",
        "\t0.03             0.171            0.006                  [ 0.  0.]\n",
        "\t0.018            0.135            0.004                  [ 0.  0.]\n",
        "\t0.035            0.185            0.006                  [ 0.  0.]\n",
        "\t0.054            0.226            0.008                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('B', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.119           1.994            0.066            [-3.865  3.961]\n",
        "\t0.21             1.521            0.138            [-2.746  2.993]\n",
        "\t0.964            0.849            0.082            [-0.628  2.598]\n",
        "\t0.064            1.968            0.113            [-3.804  3.921]\n",
        "\t0.055            1.963            0.163            [-3.743  3.991]\n",
        "\t-0.03            1.846            0.163            [-3.543  3.553]\n",
        "\t-0.139           1.998            0.143            [-4.278  3.787]\n",
        "\t-0.115           1.891            0.063            [-3.722  3.784]\n",
        "\t-0.027           1.921            0.09             [-3.6    3.777]\n",
        "\t-0.062           2.042            0.1              [-4.21   3.811]\n",
        "\t0.037            2.003            0.066            [-3.99   3.793]\n",
        "\t0.037            1.991            0.152            [-3.716  4.118]\n",
        "\t0.123            1.947            0.056            [-3.628  4.012]\n",
        "\t0.077            1.921            0.078            [-3.87   3.669]\n",
        "\t0.189            1.981            0.136            [-3.461  4.081]\n",
        "\t0.032            1.899            0.135            [-3.851  3.592]\n",
        "\t0.05             1.971            0.087            [-3.857  3.857]\n",
        "\t0.061            2.026            0.077            [-4.215  3.861]\n",
        "\t1.4              0.979            0.097            [-0.054  3.251]\n",
        "\t0.004            1.93             0.114            [-3.582  4.005]\n",
        "\t-0.034           1.912            0.114            [-3.827  3.713]\n",
        "\t0.938            2.067            0.194            [-3.105  5.102]\n",
        "\t-0.008           2.028            0.189            [-4.332  3.372]\n",
        "\t-0.238           1.948            0.137            [-4.227  3.662]\n",
        "\t0.405            1.877            0.091            [-3.593  3.762]\n",
        "\t-0.152           1.906            0.037            [-3.776  3.686]\n",
        "\t-0.02            1.941            0.05             [-4.019  3.687]\n",
        "\t0.152            2.004            0.072            [-3.691  4.172]\n",
        "\t-0.235           1.723            0.143            [-3.596  3.105]\n",
        "\t-0.08            1.964            0.033            [-3.805  3.858]\n",
        "\t-0.337           2.086            0.185            [-4.418  3.653]\n",
        "\t0.006            1.981            0.058            [-3.938  3.822]\n",
        "\t-3.825           0.705            0.07             [-5.174 -2.632]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.858           -1.489          -0.24          1.193         3.997\n",
        "\t-2.75            -0.875          0.191          1.344         2.992\n",
        "\t-0.697           0.385           0.992          1.56          2.56\n",
        "\t-3.907           -1.252          0.136          1.388         3.886\n",
        "\t-3.984           -1.235          0.071          1.368         3.844\n",
        "\t-3.499           -1.296          -0.096         1.251         3.613\n",
        "\t-4.194           -1.395          -0.117         1.134         3.894\n",
        "\t-3.842           -1.357          -0.171         1.147         3.684\n",
        "\t-3.738           -1.321          -0.028         1.275         3.661\n",
        "\t-4.136           -1.332          -0.039         1.241         3.922\n",
        "\t-3.819           -1.305          0.06           1.363         3.986\n",
        "\t-3.925           -1.248          0.013          1.329         3.967\n",
        "\t-3.855           -1.161          0.197          1.423         3.847\n",
        "\t-3.837           -1.176          0.143          1.364         3.728\n",
        "\t-3.531           -1.217          0.14           1.549         4.055\n",
        "\t-3.849           -1.206          0.079          1.333         3.603\n",
        "\t-3.85            -1.276          0.072          1.377         3.874\n",
        "\t-4.048           -1.238          0.059          1.377         4.046\n",
        "\t-0.28            0.619           1.15           2.289         3.12\n",
        "\t-3.77            -1.248          -0.055         1.28          3.861\n",
        "\t-3.621           -1.294          -0.134         1.176         3.965\n",
        "\t-3.021           -0.351          0.94           2.17          5.302\n",
        "\t-4.911           -1.222          0.184          1.393         3.247\n",
        "\t-4.555           -1.395          -0.08          0.99          3.432\n",
        "\t-3.708           -0.819          0.663          1.713         3.676\n",
        "\t-3.753           -1.464          -0.266         1.112         3.72\n",
        "\t-3.879           -1.268          -0.047         1.234         3.869\n",
        "\t-3.783           -1.203          0.154          1.492         4.117\n",
        "\t-3.438           -1.477          -0.283         0.979         3.274\n",
        "\t-3.89            -1.401          -0.089         1.232         3.798\n",
        "\t-4.338           -1.789          -0.382         1.17          3.839\n",
        "\t-3.819           -1.319          -0.018         1.313         4.006\n",
        "\t-5.169           -4.371          -3.795         -3.329        -2.621\n",
        "\t\n",
        "\n",
        "outlier_('E', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.006            0.078            0.003                  [ 0.  0.]\n",
        "\t0.003            0.053            0.002                  [ 0.  0.]\n",
        "\t0.001            0.029            0.001                  [ 0.  0.]\n",
        "\t0.0              0.008            0.0                    [ 0.  0.]\n",
        "\t0.009            0.096            0.004                  [ 0.  0.]\n",
        "\t0.08             0.271            0.018                  [ 0.  1.]\n",
        "\t0.005            0.073            0.003                  [ 0.  0.]\n",
        "\t0.008            0.087            0.003                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.005            0.074            0.003                  [ 0.  0.]\n",
        "\t0.005            0.073            0.003                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.007            0.086            0.004                  [ 0.  0.]\n",
        "\t0.007            0.084            0.004                  [ 0.  0.]\n",
        "\t0.005            0.067            0.002                  [ 0.  0.]\n",
        "\t0.015            0.122            0.007                  [ 0.  0.]\n",
        "\t0.006            0.077            0.003                  [ 0.  0.]\n",
        "\t0.01             0.099            0.005                  [ 0.  0.]\n",
        "\t0.012            0.107            0.006                  [ 0.  0.]\n",
        "\t0.002            0.048            0.002                  [ 0.  0.]\n",
        "\t0.009            0.094            0.008                  [ 0.  0.]\n",
        "\t0.005            0.068            0.003                  [ 0.  0.]\n",
        "\t0.003            0.053            0.002                  [ 0.  0.]\n",
        "\t0.304            0.46             0.038                  [ 0.  1.]\n",
        "\t0.542            0.498            0.046                  [ 0.  1.]\n",
        "\t0.001            0.025            0.001                  [ 0.  0.]\n",
        "\t0.003            0.051            0.002                  [ 0.  0.]\n",
        "\t0.014            0.116            0.008                  [ 0.  0.]\n",
        "\t0.01             0.101            0.005                  [ 0.  0.]\n",
        "\t0.017            0.129            0.007                  [ 0.  0.]\n",
        "\t0.003            0.058            0.002                  [ 0.  0.]\n",
        "\t0.003            0.056            0.002                  [ 0.  0.]\n",
        "\t0.004            0.065            0.002                  [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            1.0           1.0\n",
        "\t0.0              0.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('F', 'LB_IPTG'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.768            1.769            0.159            [-2.866  3.803]\n",
        "\t0.515            1.901            0.185            [-2.934  4.094]\n",
        "\t1.473            0.452            0.045            [ 0.753  2.2  ]\n",
        "\t0.609            1.923            0.167            [-2.862  4.297]\n",
        "\t0.67             1.418            0.138            [-2.017  3.363]\n",
        "\t0.152            1.936            0.135            [-3.572  3.926]\n",
        "\t-0.075           2.013            0.137            [-3.644  3.981]\n",
        "\t0.318            2.072            0.164            [-3.718  4.329]\n",
        "\t-1.603           0.111            0.008            [-1.809 -1.38 ]\n",
        "\t0.67             1.904            0.172            [-2.929  4.462]\n",
        "\t0.314            1.897            0.126            [-3.46   3.783]\n",
        "\t-0.485           0.529            0.027            [-1.342  0.832]\n",
        "\t0.418            1.918            0.172            [-3.428  3.906]\n",
        "\t-0.122           1.954            0.181            [-5.03   3.209]\n",
        "\t-0.273           2.09             0.116            [-4.283  3.752]\n",
        "\t-0.189           2.24             0.192            [-5.109  4.082]\n",
        "\t1.525            0.388            0.038            [ 0.92   2.469]\n",
        "\t-0.023           0.598            0.059            [-0.98   1.376]\n",
        "\t-0.234           1.736            0.168            [-3.698  3.   ]\n",
        "\t-0.093           1.986            0.136            [-3.927  3.745]\n",
        "\t2.175            1.495            0.149            [-0.391  4.687]\n",
        "\t-0.062           2.137            0.12             [-4.201  4.131]\n",
        "\t-0.564           0.36             0.036            [-1.198  0.041]\n",
        "\t-0.058           1.884            0.114            [-3.826  3.604]\n",
        "\t0.417            2.502            0.245            [-4.772  4.352]\n",
        "\t-1.492           0.119            0.002            [-1.726 -1.26 ]\n",
        "\t1.201            1.137            0.111            [-1.055  3.213]\n",
        "\t0.025            1.885            0.137            [-3.741  3.671]\n",
        "\t-0.046           1.922            0.131            [-3.962  3.723]\n",
        "\t0.082            1.929            0.138            [-3.589  3.787]\n",
        "\t-0.581           1.923            0.19             [-4.079  2.782]\n",
        "\t0.036            2.049            0.156            [-3.748  4.27 ]\n",
        "\t-0.793           1.772            0.171            [-4.194  2.557]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.921           -0.415          0.97           2.055         3.764\n",
        "\t-2.902           -0.904          0.452          1.92          4.141\n",
        "\t0.758            1.058           1.472          1.913         2.209\n",
        "\t-2.943           -0.703          0.549          1.921         4.255\n",
        "\t-2.673           -0.313          0.684          1.802         3.085\n",
        "\t-3.644           -1.143          0.09           1.467         3.904\n",
        "\t-3.827           -1.457          -0.04          1.317         3.81\n",
        "\t-3.637           -1.109          0.256          1.73          4.486\n",
        "\t-1.808           -1.68           -1.604         -1.532        -1.377\n",
        "\t-2.925           -0.665          0.662          1.996         4.474\n",
        "\t-3.338           -1.063          0.325          1.662         3.949\n",
        "\t-1.398           -0.576          -0.495         -0.407        0.786\n",
        "\t-3.288           -0.966          0.532          1.747         4.165\n",
        "\t-4.21            -1.378          -0.115         1.189         4.113\n",
        "\t-4.249           -1.685          -0.268         1.097         3.82\n",
        "\t-4.867           -1.533          -0.124         1.175         4.454\n",
        "\t0.869            1.237           1.517          1.76          2.424\n",
        "\t-1.156           -0.462          -0.036         0.33          1.265\n",
        "\t-3.695           -1.36           -0.16          0.964         3.014\n",
        "\t-3.888           -1.528          -0.044         1.292         3.811\n",
        "\t-0.472           0.913           2.409          3.184         4.647\n",
        "\t-4.072           -1.578          -0.099         1.398         4.316\n",
        "\t-1.166           -0.86           -0.515         -0.343        0.149\n",
        "\t-4.008           -1.224          -0.013         1.204         3.487\n",
        "\t-4.508           -1.5            0.9            2.115         4.794\n",
        "\t-1.722           -1.571          -1.493         -1.414        -1.253\n",
        "\t-1.401           0.463           1.257          2.112         2.991\n",
        "\t-3.77            -1.173          0.048          1.27          3.644\n",
        "\t-4.048           -1.223          -0.013         1.218         3.67\n",
        "\t-3.629           -1.305          0.055          1.458         3.764\n",
        "\t-3.966           -2.327          -0.4           0.869         3.015\n",
        "\t-4.031           -1.345          0.058          1.437         4.051\n",
        "\t-4.183           -2.203          -0.682         0.54          2.588\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('B', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-0.033           2.0              0.017            [-3.907  3.871]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.899           -1.39           -0.022         1.322         3.908\n",
        "\t\n",
        "\n",
        "outlier_('D', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.013            0.113            0.006                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.002            0.04             0.002                  [ 0.  0.]\n",
        "\t0.006            0.076            0.004                  [ 0.  0.]\n",
        "\t0.002            0.042            0.001                  [ 0.  0.]\n",
        "\t0.001            0.033            0.001                  [ 0.  0.]\n",
        "\t0.002            0.047            0.001                  [ 0.  0.]\n",
        "\t0.002            0.046            0.001                  [ 0.  0.]\n",
        "\t0.003            0.053            0.002                  [ 0.  0.]\n",
        "\t0.006            0.079            0.004                  [ 0.  0.]\n",
        "\t0.005            0.073            0.005                  [ 0.  0.]\n",
        "\t0.004            0.064            0.003                  [ 0.  0.]\n",
        "\t0.006            0.079            0.003                  [ 0.  0.]\n",
        "\t0.004            0.061            0.003                  [ 0.  0.]\n",
        "\t0.964            0.186            0.016                  [ 1.  1.]\n",
        "\t0.002            0.04             0.001                  [ 0.  0.]\n",
        "\t0.083            0.276            0.022                  [ 0.  1.]\n",
        "\t0.009            0.095            0.005                  [ 0.  0.]\n",
        "\t0.001            0.025            0.001                  [ 0.  0.]\n",
        "\t0.004            0.061            0.002                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.143            0.351            0.027                  [ 0.  1.]\n",
        "\t0.001            0.037            0.001                  [ 0.  0.]\n",
        "\t0.005            0.068            0.002                  [ 0.  0.]\n",
        "\t0.041            0.197            0.01                   [ 0.  0.]\n",
        "\t0.004            0.061            0.002                  [ 0.  0.]\n",
        "\t0.007            0.085            0.004                  [ 0.  0.]\n",
        "\t0.002            0.045            0.001                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.003            0.052            0.002                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('F', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.062            0.242            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_dist_('F', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.178            1.993            0.13             [-3.793  3.863]\n",
        "\t0.642            1.512            0.148            [-2.566  3.49 ]\n",
        "\t-0.344           1.813            0.144            [-4.109  3.174]\n",
        "\t0.146            1.82             0.173            [-2.92   4.006]\n",
        "\t0.581            1.74             0.165            [-2.119  4.345]\n",
        "\t0.332            1.94             0.152            [-3.704  3.856]\n",
        "\t-0.07            1.949            0.069            [-3.913  3.683]\n",
        "\t-0.323           1.907            0.136            [-3.915  3.617]\n",
        "\t-1.688           0.142            0.002            [-1.964 -1.41 ]\n",
        "\t-0.004           1.731            0.157            [-3.309  3.553]\n",
        "\t-0.611           2.453            0.241            [-4.915  4.305]\n",
        "\t-0.22            2.014            0.167            [-4.002  3.913]\n",
        "\t0.955            1.697            0.152            [-2.62   4.174]\n",
        "\t0.007            2.113            0.156            [-4.032  4.054]\n",
        "\t0.066            2.041            0.156            [-3.786  4.171]\n",
        "\t-0.185           1.852            0.175            [-4.197  2.852]\n",
        "\t0.001            1.941            0.136            [-3.787  3.736]\n",
        "\t-0.157           1.961            0.185            [-4.484  3.391]\n",
        "\t0.065            2.061            0.083            [-3.944  3.959]\n",
        "\t0.121            1.877            0.162            [-3.563  3.673]\n",
        "\t0.006            2.09             0.106            [-4.135  4.109]\n",
        "\t-0.038           1.958            0.184            [-4.305  3.795]\n",
        "\t0.255            2.033            0.158            [-3.457  4.485]\n",
        "\t-0.182           2.002            0.138            [-4.047  3.543]\n",
        "\t-0.226           2.046            0.175            [-4.156  3.5  ]\n",
        "\t-4.157           0.144            0.002            [-4.431 -3.865]\n",
        "\t0.278            1.932            0.185            [-4.094  3.8  ]\n",
        "\t-0.1             1.974            0.165            [-4.327  3.625]\n",
        "\t-0.157           1.999            0.114            [-4.159  3.761]\n",
        "\t0.304            1.893            0.17             [-3.752  3.618]\n",
        "\t0.551            1.939            0.153            [-3.287  4.091]\n",
        "\t0.112            1.996            0.119            [-3.821  4.065]\n",
        "\t0.174            1.685            0.159            [-2.798  3.581]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.823           -1.129          0.242          1.561         3.836\n",
        "\t-2.421           -0.378          0.604          1.724         3.684\n",
        "\t-4.194           -1.431          -0.284         0.875         3.134\n",
        "\t-2.951           -1.267          0.125          1.292         3.986\n",
        "\t-2.165           -0.726          0.294          1.772         4.322\n",
        "\t-3.55            -0.928          0.352          1.676         4.039\n",
        "\t-3.763           -1.431          -0.102         1.234         3.858\n",
        "\t-3.863           -1.583          -0.4           0.901         3.674\n",
        "\t-1.962           -1.781          -1.688         -1.597        -1.403\n",
        "\t-3.416           -1.183          0.024          1.142         3.472\n",
        "\t-5.875           -2.151          -0.607         0.981         3.761\n",
        "\t-3.896           -1.591          -0.276         1.057         4.104\n",
        "\t-2.329           -0.223          0.947          2.084         4.541\n",
        "\t-3.925           -1.488          -0.094         1.458         4.196\n",
        "\t-3.809           -1.398          0.069          1.44          4.154\n",
        "\t-3.964           -1.517          -0.023         1.112         3.181\n",
        "\t-3.892           -1.293          0.073          1.331         3.637\n",
        "\t-4.676           -1.24           -0.229         1.231         3.271\n",
        "\t-3.973           -1.378          0.091          1.47          3.945\n",
        "\t-3.965           -1.113          0.275          1.404         3.439\n",
        "\t-4.116           -1.364          -0.011         1.395         4.167\n",
        "\t-4.171           -1.071          0.199          1.109         3.977\n",
        "\t-3.684           -1.13           0.227          1.576         4.312\n",
        "\t-4.027           -1.545          -0.174         1.215         3.591\n",
        "\t-4.571           -1.608          -0.076         1.298         3.252\n",
        "\t-4.44            -4.254          -4.156         -4.062        -3.872\n",
        "\t-3.912           -0.892          0.339          1.539         4.016\n",
        "\t-4.153           -1.365          -0.111         1.166         3.873\n",
        "\t-4.097           -1.444          -0.137         1.141         3.851\n",
        "\t-3.67            -0.882          0.442          1.642         3.744\n",
        "\t-3.231           -0.762          0.576          1.938         4.177\n",
        "\t-3.811           -1.228          0.12           1.404         4.086\n",
        "\t-2.78            -1.122          0.13           1.327         3.614\n",
        "\t\n",
        "\n",
        "outlier_dist_('C', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.639            1.777            0.157            [-2.656  4.557]\n",
        "\t-0.297           2.198            0.197            [-4.741  3.66 ]\n",
        "\t0.143            1.976            0.079            [-3.701  3.941]\n",
        "\t0.166            2.02             0.171            [-3.855  4.163]\n",
        "\t0.66             1.577            0.145            [-2.382  3.872]\n",
        "\t-0.113           0.448            0.045            [-0.982  0.47 ]\n",
        "\t-1.314           0.881            0.08               [-2.59  0.7 ]\n",
        "\t-0.186           1.992            0.064            [-4.088  3.688]\n",
        "\t0.028            2.012            0.182            [-3.5    4.466]\n",
        "\t-0.129           1.943            0.111            [-3.831  3.78 ]\n",
        "\t0.071            2.01             0.082            [-3.908  3.898]\n",
        "\t-0.098           1.827            0.125            [-3.438  3.56 ]\n",
        "\t0.041            2.112            0.13             [-4.19   4.004]\n",
        "\t-0.015           1.967            0.064            [-3.788  3.932]\n",
        "\t0.035            2.038            0.203            [-3.624  3.841]\n",
        "\t0.063            1.966            0.097            [-3.688  3.89 ]\n",
        "\t0.46             1.607            0.045            [-3.429  3.4  ]\n",
        "\t0.608            1.708            0.141            [-4.061  3.324]\n",
        "\t0.239            1.739            0.168            [-3.484  3.428]\n",
        "\t0.591            2.083            0.191            [-3.583  4.819]\n",
        "\t-0.529           1.486            0.135            [-3.217  2.312]\n",
        "\t0.023            1.987            0.13             [-3.797  3.949]\n",
        "\t-0.052           2.052            0.116            [-3.968  4.063]\n",
        "\t-0.138           1.742            0.106            [-3.587  3.206]\n",
        "\t0.008            1.846            0.138            [-3.445  3.678]\n",
        "\t-0.275           2.122            0.179            [-4.602  3.549]\n",
        "\t-0.045           1.962            0.12             [-3.992  3.638]\n",
        "\t0.008            1.915            0.107            [-3.707  3.745]\n",
        "\t-0.036           2.036            0.136            [-3.894  4.012]\n",
        "\t0.017            2.016            0.099            [-3.755  4.023]\n",
        "\t-0.005           1.984            0.133            [-4.029  3.756]\n",
        "\t-0.118           2.017            0.178            [-4.121  3.788]\n",
        "\t-0.09            2.053            0.12             [-4.219  3.776]\n",
        "\t0.168            1.985            0.183            [-3.664  3.863]\n",
        "\t-0.187           1.26             0.124            [-2.812  2.028]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.696           -0.583          0.56           1.724         4.54\n",
        "\t-4.606           -1.821          -0.28          1.257         3.855\n",
        "\t-3.725           -1.184          0.193          1.476         3.937\n",
        "\t-3.597           -1.203          0.056          1.506         4.598\n",
        "\t-2.59            -0.382          0.629          1.718         3.768\n",
        "\t-0.986           -0.344          -0.034         0.266         0.469\n",
        "\t-2.291           -1.715          -1.46          -1.17         1.96\n",
        "\t-4.049           -1.541          -0.196         1.134         3.746\n",
        "\t-4.164           -1.276          0.084          1.33          4.076\n",
        "\t-4.029           -1.4            -0.111         1.146         3.649\n",
        "\t-3.86            -1.272          0.123          1.415         3.981\n",
        "\t-3.476           -1.398          -0.14          1.162         3.55\n",
        "\t-4.276           -1.368          0.119          1.477         3.931\n",
        "\t-3.904           -1.35           0.019          1.292         3.838\n",
        "\t-3.48            -1.053          -0.423         1.353         4.171\n",
        "\t-3.76            -1.276          0.019          1.403         3.838\n",
        "\t-3.449           -0.3            0.855          1.311         3.394\n",
        "\t-3.861           0.208           1.055          1.46          3.585\n",
        "\t-3.616           -0.788          0.313          1.45          3.329\n",
        "\t-4.781           -0.485          0.8            1.97          4.241\n",
        "\t-3.508           -1.615          -0.495         0.558         2.152\n",
        "\t-3.881           -1.337          0.081          1.348         3.879\n",
        "\t-3.925           -1.445          -0.125         1.325         4.146\n",
        "\t-3.509           -1.255          -0.245         1.091         3.304\n",
        "\t-3.582           -1.244          -0.055         1.275         3.592\n",
        "\t-4.492           -1.687          -0.286         1.145         3.717\n",
        "\t-3.988           -1.347          -0.067         1.293         3.65\n",
        "\t-3.919           -1.273          0.143          1.254         3.606\n",
        "\t-4.092           -1.377          -0.061         1.37          3.901\n",
        "\t-3.787           -1.338          -0.017         1.383         4.013\n",
        "\t-4.054           -1.256          -0.023         1.332         3.75\n",
        "\t-4.087           -1.444          -0.035         1.192         3.834\n",
        "\t-4.394           -1.43           -0.05          1.356         3.666\n",
        "\t-3.704           -1.246          0.26           1.581         3.85\n",
        "\t-2.971           -1.009          -0.136         0.829         1.881\n",
        "\t\n",
        "\n",
        "outlier_dist_sim_('E', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.004            1.994            0.016            [-4.002  3.865]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.93            -1.341          0.018          1.334         3.975\n",
        "\t\n",
        "\n",
        "outlier_('F', 'LB'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.0              0.021            0.0                    [ 0.  0.]\n",
        "\t0.002            0.049            0.002                  [ 0.  0.]\n",
        "\t0.011            0.106            0.006                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.005            0.07             0.003                  [ 0.  0.]\n",
        "\t0.0              0.019            0.0                    [ 0.  0.]\n",
        "\t0.001            0.032            0.001                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.009            0.094            0.005                  [ 0.  0.]\n",
        "\t0.017            0.129            0.006                  [ 0.  0.]\n",
        "\t0.005            0.072            0.004                  [ 0.  0.]\n",
        "\t0.009            0.093            0.005                  [ 0.  0.]\n",
        "\t0.007            0.081            0.003                  [ 0.  0.]\n",
        "\t0.001            0.03             0.001                  [ 0.  0.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.001            0.024            0.001                  [ 0.  0.]\n",
        "\t0.016            0.126            0.007                  [ 0.  0.]\n",
        "\t0.0              0.021            0.0                    [ 0.  0.]\n",
        "\t0.0              0.012            0.0                    [ 0.  0.]\n",
        "\t0.0              0.0              0.0                    [ 0.  0.]\n",
        "\t0.003            0.058            0.003                  [ 0.  0.]\n",
        "\t0.001            0.025            0.001                  [ 0.  0.]\n",
        "\t0.003            0.05             0.002                  [ 0.  0.]\n",
        "\t1.0              0.0              0.0                    [ 1.  1.]\n",
        "\t0.003            0.055            0.002                  [ 0.  0.]\n",
        "\t0.009            0.097            0.003                  [ 0.  0.]\n",
        "\t0.008            0.091            0.006                  [ 0.  0.]\n",
        "\t0.001            0.036            0.001                  [ 0.  0.]\n",
        "\t0.001            0.037            0.001                  [ 0.  0.]\n",
        "\t0.001            0.038            0.001                  [ 0.  0.]\n",
        "\t0.027            0.161            0.01                   [ 0.  0.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t1.0              1.0             1.0            1.0           1.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           0.0\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('A', 'LB_IPTG'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.057            0.232            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "outlier_sim_('C', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.062            0.241            0.002                  [ 0.  1.]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.0              0.0             0.0            0.0           1.0\n",
        "\t\n",
        "\n",
        "true_val_('B', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.617           0.142            0.003            [-2.885 -2.329]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-2.891           -2.716          -2.618         -2.521        -2.334\n",
        "\t\n",
        "\n",
        "true_val_('A', 'LB'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t-2.93            0.118            0.003            [-3.16  -2.697]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-3.158           -3.009          -2.932         -2.854        -2.693\n",
        "\t\n",
        "\n",
        "std_('E', 'LB_IPTG_GLU'):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.115            0.022            0.001            [ 0.073  0.156]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.076            0.099           0.115          0.13          0.161\n",
        "\t\n",
        "\n",
        "std_('D', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.106            0.019            0.001            [ 0.072  0.143]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.074            0.091           0.104          0.118         0.147\n",
        "\t\n",
        "\n",
        "std_('B', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.081            0.013            0.001            [ 0.057  0.106]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.059            0.071           0.08           0.089         0.11\n",
        "\t\n",
        "\n",
        "msd_val_sim_('E', 'LB_IPTG_GLU'):\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.058            0.515            0.004            [-0.433  0.511]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t-0.368           -0.03           0.054          0.137         0.643\n",
        "\t\n"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(outl.mean(axis=0), 'd')\n",
      "plt.plot(true_val.mean() - values, 'x')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "[<matplotlib.lines.Line2D at 0x9dd7690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAECCAYAAAAW+Nd4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8886290>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(outl.mean(axis=0), 'd')\n",
      "plt.plot(true_val.mean() - values, 'x')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "[<matplotlib.lines.Line2D at 0xbcd1390>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAECCAYAAAAW+Nd4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBw9QWgC9YT1RAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbcc40d0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#a = np.array([ 0.133,  0.008,  0.002,  0.004,  0.002,  0.001])\n",
      "#a = np.array([-3.9633163 , -4.50986001, -4.96184513, -5.11599581, -5.80914299,\n",
      "#       -4.82831374])\n",
      "#sm.robust.scale.huber(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}