PyMC Bayesian Modeling

概要

The PyMC Bayesian Modeling skill provides a comprehensive framework for building, fitting, and validating probabilistic models within Claude. It streamlines the complex Bayesian workflow—from data preparation and prior predictive checks to MCMC sampling and posterior diagnostics—ensuring that users follow best practices for hierarchical modeling and uncertainty quantification. By incorporating specialized diagnostic scripts and model comparison techniques like LOO and WAIC, this skill helps researchers and data scientists minimize sampling errors, resolve divergences, and generate reliable statistical inferences using modern PyMC 5.x standards.

主な機能

• Standardized MCMC sampling workflow using NUTS for high-dimensional models.
• Integrated Variational Inference (ADVI) for fast model exploration and initialization.
• Pre-configured templates for hierarchical, linear, logistic, and time-series models.
• 3,718 GitHub stars
• Advanced model comparison using LOO/WAIC and Pareto-k reliability checks.
• Automated diagnostics for R-hat, Effective Sample Size (ESS), and divergences.

ユースケース

• Performing principled uncertainty quantification for predictive analytics and scientific research.
• Developing hierarchical models to account for multi-level data structures and group effects.
• Implementing robust regression models with custom priors for small or noisy datasets.