Holdout predictive checks for Bayesian model criticism

Abstract

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. In this paper we develop a new method for Bayesian model criticism, the holdout predictive check (HPC). HPCs are built on posterior predictive checks (PPCs), a seminal method that checks a model by assessing the posterior predictive distribution on the observed data. However, PPCs use the data twice---both to calculate the posterior predictive and to evaluate it---which can lead to uncalibrated $p$-values. HPCs, in contrast, compare the posterior predictive distribution to a draw from the population distribution, a heldout dataset. This method blends Bayesian modeling with frequentist assessment. Unlike the PPC, we prove that the HPC is properly calibrated. Empirically, we study HPCs on classical regression, a hierarchical model of text data, and factor analysis.