pymc-bayesian-modeling

SKILL.md

PyMC Bayesian Modeling

Overview

PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).

When to Use This Skill

This skill should be used when:

Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)

Performing MCMC sampling or variational inference

Conducting prior/posterior predictive checks

Diagnosing sampling issues (divergences, convergence, ESS)

Comparing multiple models using information criteria (LOO, WAIC)

Implementing uncertainty quantification through Bayesian methods

Working with hierarchical/multilevel data structures

Handling missing data or measurement error in a principled way

Standard Bayesian Workflow