pymc

Topics in the source

• PyMC Bayesian Modeling
• Overview
• When to Use This Skill
• Standard Bayesian Workflow
• 1. Data Preparation
• 2. Model Building
• 3. Prior Predictive Check
• 4. Fit Model

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). 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 Follow this workflow for building and validating Bayesian models: Key practices: Standardize continuous predictors (improves sampling efficiency) Center outcomes when possible Handle missing data explicitly (treat as parameters) Use named dimensions with coords for clarity Key practices: Use weakly informative priors (not flat priors) Use HalfNormal or Exponential for scale parameters Use name

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