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ivanistheone committed Aug 21, 2024
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454 changes: 190 additions & 264 deletions _sources/notebooks/46_generalized_linear_models.ipynb

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247 changes: 247 additions & 0 deletions _sources/notebooks/50_extra_bayesian_stuff.ipynb
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"source": [
"# Chapter 5: Extra code, drafts, and cut material"
]
},
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"cell_type": "markdown",
"id": "1ad57ed2-8583-4d1f-a16c-ddd481ee3e5f",
"metadata": {},
"source": [
"## Bayesian p-value"
]
},
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"WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [beta0, beta1, sigma]\n",
"WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
"WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
"WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
"WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
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"source": [
"import pymc as pm\n",
"import numpy as np\n",
"import arviz as az\n",
"\n",
"# Simulated data\n",
"np.random.seed(42)\n",
"x = np.random.normal(0, 1, 100)\n",
"y = 3 + 2 * x + np.random.normal(0, 1, 100)\n",
"\n",
"# Bayesian Linear Regression Model\n",
"with pm.Model() as model:\n",
" # Priors\n",
" beta0 = pm.Normal(\"beta0\", mu=0, sigma=10)\n",
" beta1 = pm.Normal(\"beta1\", mu=0, sigma=10)\n",
" sigma = pm.HalfNormal(\"sigma\", sigma=1)\n",
" \n",
" # Likelihood\n",
" mu = beta0 + beta1 * x\n",
" y_obs = pm.Normal(\"y_obs\", mu=mu, sigma=sigma, observed=y)\n",
" \n",
" # Sampling\n",
" trace = pm.sample(2000, return_inferencedata=True)"
]
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"Sampling: [y_obs]\n"
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"# Posterior Predictive Check\n",
"# with model:\n",
"# ppc = pm.sample_posterior_predictive(trace) \n",
"# Posterior Predictive Check\n",
"ppc = pm.sample_posterior_predictive(trace, model=model)\n",
" \n"
]
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"text": [
"Bayesian p-value: 0.4925\n"
]
}
],
"source": [
"# Extract the posterior predictive samples for 'y_obs'\n",
"y_rep = ppc.posterior_predictive[\"y_obs\"].values\n",
"\n",
"# Calculate Bayesian p-value for the slope\n",
"test_stat_observed = np.mean(y) # Example test statistic\n",
"test_stat_rep = np.mean(y_rep, axis=1)\n",
"bayesian_p_value = np.mean(test_stat_rep >= test_stat_observed)\n",
"print(\"Bayesian p-value:\", bayesian_p_value)"
]
},
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531 changes: 531 additions & 0 deletions _sources/notebooks/51_intro_to_Bayesian_stats.ipynb

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