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pymc

Builds, fits, checks, and compares Bayesian models in Python with PyMC and ArviZ. Covers hierarchical (multilevel) models, NUTS MCMC sampling, variational inference (ADVI), prior and posterior predictive checks, convergence diagnostics (R-hat, ESS, divergences), and LOO/WAIC model comparison. Use when writing a PyMC model for regression, count, or binary data. Use when fitting a hierarchical model with partial pooling. Use when diagnosing divergences, low ESS, or high R-hat. Use when comparing candidate models with LOO. Use when choosing priors or running prior predictive checks. Not for non-Bayesian regression; use statsmodels or scikit-learn instead.
Category: data-science-and-ml · License: Apache License, Version 2.0 · Version: 1.4

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Install

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When to use it

Builds, fits, checks, and compares Bayesian models in Python with PyMC and ArviZ. Covers hierarchical (multilevel) models, NUTS MCMC sampling, variational inference (ADVI), prior and posterior predictive checks, convergence diagnostics (R-hat, ESS, divergences), and LOO/WAIC model comparison. Use when writing a PyMC model for regression, count, or binary data. Use when fitting a hierarchical model with partial pooling. Use when diagnosing divergences, low ESS, or high R-hat. Use when comparing candidate models with LOO. Use when choosing priors or running prior predictive checks. Not for non-Bayesian regression; use statsmodels or scikit-learn instead.

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Full playbook

Read SKILL.md for the complete workflow, references and any scripts. The agent installer copies the full skill folder.