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The PyMC Bayesian Modeling skill provides a comprehensive toolkit for building robust statistical models within Claude Code, automating the implementation of hierarchical structures, MCMC sampling, and variational inference. It guides developers through the entire Bayesian workflow—from prior predictive checks to sophisticated model comparisons using LOO and WAIC—ensuring that statistical inferences are both accurate and reliable. This skill is particularly useful for data scientists and researchers who need to quantify uncertainty, handle missing data, or implement complex multilevel models with best-practice patterns like non-centered parameterization.
