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{
"metadata": {
"name": "",
"signature": "sha256:44c46e4a2709324a2bb916d1913873b0eae1a7e831491cfdb6186fa719412afc"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"!date"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Wed May 14 07:19:24 PDT 2014\r\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If pymc.numpy.random.seed(0) guarantee the same random number sequence to initialize a stochastic variable (say a Uniform distribution), why does its posterior samples (from trace plot) don't have the same values for multiple runs with the same seed=0 ?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np, pymc as pm\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# what pymc version is this?\n",
"\n",
"pm.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"'2.3.2'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.seed(123456)\n",
"a = pm.Normal('a', 0, 1)\n",
"pm.MCMC([a]).sample(1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 1000 of 1000 complete in 0.1 sec"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.seed(123456)\n",
"b = pm.Normal('b', 0, 1)\n",
"pm.MCMC([b]).sample(1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 1000 of 1000 complete in 0.1 sec"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.sum((a.trace() - b.trace())**2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"0.0"
]
}
],
"prompt_number": 6
}
],
"metadata": {}
}
]
}
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