bayesian-pymc

build, review, debug, and explain bayesian analyses in python using pymc, arviz, altair, pandas, and numpy. use when the user wants to create a new bayesian workflow from a dataset or research question, translate frequentist work into bayesian form, inspect or improve an existing pymc model or notebook, interpret priors/posteriors/predictive checks, compare models, or produce teaching-oriented explanations in the style of statistical rethinking with explicit generative assumptions, causal reasoning, and workflow discipline.