bayesian_model_diceRoll.ipynb

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      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
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    "import pymc3 as pm"
   ]
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      "C:\\Users\\Vaneeza Ahmad\\anaconda3\\envs\\pm3bap\\lib\\site-packages\\deprecat\\classic.py:215: FutureWarning: In v4.0, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
      "  return wrapped_(*args_, **kwargs_)\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
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       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
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      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 23 seconds.\n"
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   "source": [
    "total_rolls=150\n",
    "no_observed_six=39\n",
    "\n",
    "with pm.Model() as my_model:\n",
    "    #supposing prior = Beta\n",
    "    p=pm.Beta(\"p\", alpha=1, beta=1)\n",
    "    \n",
    "    #supposing prior = Uniform\n",
    "    #p=pm.Uniform(\"p\", 0, 1)\n",
    "    \n",
    "    #supposing likelihood = Binomial bcz number of successes and trials is given\n",
    "    likelihood=pm.Binomial(\"likelihood\", p=p, n=total_rolls, observed=no_observed_six)\n",
    "    data=pm.sample(1000)"
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    "data['p'].mean()"
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   "source": [
    "# Model Estimations Results\n",
    "| Choice of Prior | Parameters     | Model Estimate |\n",
    "|-----------------|----------------|----------------|\n",
    "|Uniform          |lower=0 upper=1 |0.26368161969593|\n",
    "|Beta             |alpha=1 beta=1  |0.26149624983608|\n",
    "|Beta             |alpha=5 beta=1  |0.28081282164154|\n",
    "|Beta             |alpha=1 beta=5  |0.25661071815007|\n",
    "|Beta             |alpha=5 beta=5  |0.27391507401693|\n"
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   "id": "8e0d6a14-7b3e-438c-8a28-9cca621f3337",
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   "source": [
    "#BY Vaneeza Ahmad (21i-0390) CS-A"
   ]
  }
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