소개
This skill provides a comprehensive framework for Bayesian inference and probabilistic programming using PyMC. It equips developers and data scientists with structured workflows for MCMC sampling (NUTS), variational inference, and hierarchical modeling, ensuring robust uncertainty quantification and model validation. By integrating domain-specific best practices for prior selection, posterior checks, and sampling diagnostics, it helps users avoid common pitfalls like divergences or low effective sample sizes while enabling advanced tasks like time series analysis and model comparison using LOO and WAIC.
