Bayesian Asymptotics with Misspecified Models
Stephen G. Walker1, Pierpaolo De Blasi2
1University of Kent; 2University of Turin

We study the asymptotic properties of a sequence of posterior distributions based on an independent and identically distributed sample and the Bayesian model is misspecified. We find a suffcient condition on the prior for the posterior to accumulate around the density in the model closest in the Kullback– Leibler sense to the true density function. Examples are presented.

Keywords: Asymptotics; Consistency; Misspecified model

Biography: Professor of Statistics