Bayesian asymptotics with misspecified models

Abstract

In this paper, we study the asymptotic properties of a sequence of posterior distributions based on an independent and identically distributed sample and when the Bayesian model is misspecified.We find a sufficient condition on the prior for the posterior to accumulate around the densities in the model closest in the Kullback-Leibler sense to the true density function.Examples are presented.