BAYESIAN ASYMPTOTICS WITH MISSPECIFIED MODELS

BAYESIAN ASYMPTOTICS WITH MISSPECIFIED MODELS

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 m...

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Veröffentlicht in:Statistica Sinica 2013-01, Vol.23 (1), p.169-187
Hauptverfasser: De Blasi, Pierpaolo, Walker, Stephen G.
Format: Artikel
Sprache:eng
Schlagworte:
Covariance
Density estimation
Entropy
Infinity
Mathematical theorems
Polynomials
Sufficient conditions
Truncation
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Beschreibung
Zusammenfassung: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.
ISSN:1017-0405
1996-8507
DOI:10.5705/ss.2010.239