pymc

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 6.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, posterior predictive checks, and model comparison (LOO, WAIC).

Current Version and Setup

PyMC 6.0.1 is the current stable release as of June 2026. It requires Python 3.12+, uses PyTensor 3 as the computational graph backend, and defaults to compiled backends such as Numba. For reproducible local environments, pin the version:

uv pip install "pymc[nutpie]==6.0.1"

The nutpie extra enables the faster Rust/Numba NUTS implementation. If using NumPyro or BlackJAX, install those optional sampler dependencies in the same environment and pin them in the project lockfile.

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

Never sample first and check later. The eight-step workflow — documented with code in references/standard_workflow.md — is:

1. Data preparation — including standardizing predictors so priors are interpretable.
2. Model building — priors and likelihood in a pm.Model context.
3. Prior predictive check — confirm the priors imply plausible data before fitting.
4. Fit model — pm.sample() with an explicit seed.
5. Check diagnostics — R-hat, ESS, divergences. Divergences invalidate the fit; fix the model or reparameterize rather than raising target_accept and hoping.
6. Posterior predictive check — does the fitted model reproduce the observed data?
7. Analyze results — summaries and intervals from the posterior.
8. Make predictions — on new data via pm.set_data and posterior predictive sampling.