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\footnotesize \noindent Statistica Sinica {\bf 23} (2013), 169-187

\footnotesize \noindent doi:http://dx.doi.org/10.5705/ss.2010.239

\vspace{3pc} \normalsize \centerline{\large\bf BAYESIAN ASYMPTOTICS
WITH MISSPECIFIED MODELS} \vspace{.25cm}
\author{Pierpaolo De Blasi$^{1,2}$ and Stephen G. Walker$^3$}
\vspace{.4cm} \centerline{\it $^1$University of Torino, $^2$Collegio
Carlo Alberto and $^3$University of Kent} \vspace{.55cm}  
\begin{quotation}
\noindent {\it 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.\par \vspace{9pt} \noindent {\it Key words
and phrases:} Asymptotics, consistency, misspecified model. \par
\end{quotation}

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