Bayesian Model Diagnostics
Evaluates Bayesian model convergence and sampling performance using MCMC diagnostics for Stan and JAGS frameworks.
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.
Características Principales
01Visual diagnostic tools like trace plots and rank histograms
02Stan-specific divergence diagnosis and NUTS troubleshooting
030 GitHub stars
04Model comparison via LOO-CV, WAIC, and posterior predictive checks
05Comprehensive MCMC convergence assessment (Rhat, ESS bulk/tail)
06JAGS diagnostic suite including Gelman-Rubin and autocorrelation
Casos de Uso
01Validating posterior reliability in complex hierarchical Bayesian models
02Comparing multiple model fits using cross-validation and information criteria
03Fixing sampling geometry issues in Stan using reparameterization techniques