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 |
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| container_title | Statistica Sinica |
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| creator | De Blasi, Pierpaolo Walker, Stephen G. |
| description | 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. |
| doi_str_mv | 10.5705/ss.2010.239 |
| format | Article |
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| identifier | ISSN: 1017-0405 |
| ispartof | Statistica Sinica, 2013-01, Vol.23 (1), p.169-187 |
| issn | 1017-0405 1996-8507 |
| language | eng |
| recordid | cdi_crossref_primary_10_5705_ss_2010_239 |
| source | JSTOR Mathematics & Statistics; Jstor Complete Legacy; EZB-FREE-00999 freely available EZB journals |
| subjects | Covariance Density estimation Entropy Infinity Mathematical theorems Polynomials Sufficient conditions Truncation |
| title | BAYESIAN ASYMPTOTICS WITH MISSPECIFIED MODELS |
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