Holdout predictive checks for Bayesian model criticism

Understand

Bayesian modeling helps applied researchers articulate assumptions about their data and develop models tailored for specific applications.

• Thanks to good methods for approximate posterior inference, researchers can now easily build, use, and revise complicated Bayesian models for large and rich data.
• These capabilities, however, bring into focus the problem of model criticism.
• Researchers need tools to diagnose the fitness of their models, to understand where they fall short, and to guide their revision.