20221014-pymc-question.ipynb

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      "Running on PyMC v4.2.1\n",
      "Running on Aesara v2.8.6\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import numpy as np\n",
    "import pymc as pm\n",
    "\n",
    "import aesara as ae\n",
    "import aesara.tensor.math as tt\n",
    "import aesara.tensor as at\n",
    "\n",
    "print(f\"Running on PyMC v{pm.__version__}\")\n",
    "print(f\"Running on Aesara v{ae.__version__}\")"
   ]
  },
  {
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     "text": [
      "Only 3 samples in chain.\n",
      "Sequential sampling (1 chains in 1 job)\n",
      "CompoundStep\n",
      ">Metropolis: [μ]\n",
      ">Metropolis: [σ]\n",
      "Sampling 1 chain for 0 tune and 3 draw iterations (0 + 3 draws total) took 0 seconds.\n",
      "/home/aakhmetz/.local/lib/python3.10/site-packages/arviz/data/base.py:220: UserWarning: More chains (3) than draws (2). Passed array should have shape (chains, draws, *shape)\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_1256085/528024374.py:14: UserWarning: The number of samples is too small to check convergence reliably.\n",
      "  trace = pm.sample(3, pm.Metropolis(), tune=0, chains=1, progressbar=False, init='advi')\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model:    \n",
    "    D = tt.as_tensor_variable(5, dtype=\"int64\") \n",
    "    \n",
    "    μ = pm.HalfCauchy('μ', 5.0, initval = 1.0)\n",
    "    σ = pm.HalfCauchy('σ', 6.0, initval = 2.0)\n",
    "    \n",
    "    ### Lognormal distribution\n",
    "    σlog = tt.sqrt(tt.log((σ / μ)**2 + 1));\n",
    "    μlog = tt.log(μ) - σlog**2 / 2;\n",
    "    comp = pm.Lognormal.dist(mu = μlog, sigma = σlog)\n",
    "    p, _ = ae.scan(lambda t: tt.exp(pm.logcdf(comp, t + 0.5)), sequences = tt.arange(0, D + 1))\n",
    "    pm.Deterministic('p', p)\n",
    "  \n",
    "    trace = pm.sample(3, pm.Metropolis(), tune=0, chains=1, progressbar=False, init='advi')"
   ]
  },
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     "data": {
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       "array([1.        , 3.25747633, 9.57658511])"
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       "array([11.4065048 , 11.4065048 ,  9.12795217])"
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     "data": {
      "text/plain": [
       "array([[0.0005593 , 0.01719953, 0.0566348 , 0.10870443, 0.16555878,\n",
       "        0.22273289],\n",
       "       [0.0005593 , 0.01719953, 0.0566348 , 0.10870443, 0.16555878,\n",
       "        0.22273289],\n",
       "       [0.0005593 , 0.01719953, 0.0566348 , 0.10870443, 0.16555878,\n",
       "        0.22273289]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(trace.posterior['μ'][0].values)\n",
    "display(trace.posterior['σ'][0].values)\n",
    "trace.posterior['p'][0].values"
   ]
  },
  {
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   "execution_count": null,
   "id": "7faf18ef-7a13-4ea7-a79b-1111afb7d317",
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   "source": []
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