BAYESIAN ASYMPTOTICS WITH MISSPECIFIED MODELS

被引：18

作者：

De Blasi, Pierpaolo ^{[1
,2
]}

Walker, Stephen G. ^{[3
]}

机构：

[1] Univ Turin, Dept Econ & Stat, I-10134 Turin, Italy

[2] Coll Carlo Alberto, Moncalieri Torino, Italy

[3] Univ Kent, Inst Math Stat & Actuarial Sci, Canterbury CT2 7NZ, Kent, England

来源：

STATISTICA SINICA | 2013年 / 23卷 / 01期

基金：

欧洲研究理事会;

关键词：

Asymptotics; consistency; misspecified model; GAUSSIAN PROCESS PRIORS; DENSITY-ESTIMATION; POSTERIOR DISTRIBUTIONS; CONSISTENCY; INFORMATION; INCORRECT; MIXTURES; BEHAVIOR;

D O I：

10.5705/ss.2010.239

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：169 / 187

页数：19

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