pymc-bayesian-modeling

Bayesian modeling with PyMC involves defining priors, specifying likelihoods, and sampling posteriors using MCMC (NUTS) or variational inference (ADVI). PyMC handles model construction, fitting, and generates posterior samples for inference and prediction on new data.

Key diagnostics include R-hat (convergence), ESS (effective sample size), and divergences. PyMC and ArviZ compute these metrics to validate chain mixing and warn of sampling issues before relying on posterior estimates.

Model comparison uses leave-one-out (LOO) and WAIC criteria to evaluate predictive performance without overfitting. PyMC integrates with ArviZ to compute these metrics and rank candidate models by out-of-sample accuracy.

Yes. PyMC supports multilevel structures with non-centered parameterization to improve convergence. Hierarchical models pool information across groups while estimating group-level and population-level parameters.

PyMC applies to linear regression, logistic regression, time-series, and count data. It handles any probabilistic model where you specify priors, likelihoods, and observed data—solving inference and uncertainty quantification for custom model structures.

Posterior predictive checks simulate new data from the fitted model and compare it to observed data to detect systematic discrepancies. This validates whether the model captures key patterns and assumptions in your data.