On the limits of some Bayesian model evaluation statistics

Abstract: Model selection and order selection problems frequently arise in statistical practice. A popular approach to addressing these problems in the frequentist setting involves information criteria based on penalized maxima of log-likelihoods for competing models. In the Bayesian context, similar criteria are employed, replacing the maxima of log-likelihoods with their posterior expectations. Despite their popularity in applications, the large-sample behavior of these criteria -- such as the deviance information criterion (DIC), Bayesian predictive information criterion (BPIC), and widely-applicable Bayesian information criterion (WBIC) -- has received relatively little attention. In this work, we investigate the almost sure limits of these criteria and establish novel results on posterior and generalized posterior consistency, which are of independent interest. The utility of our theoretical findings is demonstrated via illustrative technical and numerical examples.