running-bayesian-workflow

Resolve divergences by applying non-centered parameterizations to hierarchical models and scaling weakly informative priors to your data, which eliminates the funnel dynamics that destabilize NUTS sampling.

Required Bayesian diagnostics include checking r_hat, ESS bulk, ESS tail, divergences, and E-BFMI, which must all pass their threshold limits to form a hard diagnostic gate before any credible interval interpretation.

Compare Bayesian models using LOO or WAIC by running posterior-predictive validation after NUTS sampling, which provides the standardized metrics needed to evaluate relative predictive accuracy across different model fits.

Yes, the Bayesian workflow supports Stan, NumPyro, brms, and rstanarm fits, applying prior-predictive checks, NUTS sampling diagnostics, and credible interval reporting uniformly across these different frameworks.

Vague default priors cause issues in Bayesian regression because they induce funnel geometries and divergences during NUTS sampling, whereas weakly informative priors scaled to your data stabilize the posterior exploration.

The best way to validate posterior-predictive checks is to enforce a hard diagnostic gate on ESS bulk, ESS tail, and divergences first, ensuring the posterior distribution is clean before comparing simulated data against observed outcomes.