About
The PyMC Bayesian Modeling skill equips Claude with specialized knowledge for performing advanced probabilistic programming and inference in Python. It provides structured workflows for the entire Bayesian lifecycle—from data standardization and prior predictive checks to MCMC sampling with the NUTS sampler and variational inference. By implementing best practices like non-centered parameterization for hierarchical models and rigorous diagnostic checks (R-hat, ESS, divergences), this skill ensures your statistical models are both robust and computationally efficient. It is ideal for data scientists and researchers needing principled uncertainty quantification and model comparison using LOO or WAIC.
