Skill UI

Structured workflow for building hierarchical Bayesian models with PyMC 5+, covering data prep, NUTS sampling, diagnostics, posterior checks, and predictive inference.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

PyMC is a powerful Python library for probabilistic programming and Bayesian statistical inference. It allows users to define complex models, including hierarchical structures, time series, and regression, and perform advanced inference using MCMC sampling (NUTS) or variational inference. This skill covers the complete workflow: model specification, prior validation, fitting, advanced diagnostics (R-hat, ESS), and posterior predictive checking, enabling rigorous uncertainty quantification.