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This skill provides a robust framework for validating Bayesian statistical models by implementing essential MCMC diagnostics. It guides users through assessing convergence metrics like Rhat and Effective Sample Size (ESS), identifying sampling issues such as divergences and treedepth limits in Stan, and performing posterior predictive checks to ensure model fit. By providing standardized code patterns for both Stan and JAGS, it helps data scientists ensure their posterior estimates are reliable and their models are statistically sound before proceeding to inference.
