Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
---
name: pymc
description: Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.12+ and PyMC 6.0.1-compatible dependencies. Install reproducible environments with `uv pip install "pymc[nutpie]==6.0.1"`; optional NumPyro or BlackJAX samplers require separately pinned JAX-compatible dependencies.
license: Apache License, Version 2.0
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---
# 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:
```bash
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.
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