文章基本信息

标题：Bayesian Estimation of the Discrepancy with Misspecified Parametric Models
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作者：Pierpaolo De Blasi ; Stephen G. Walker
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2013
卷号：8
期号：4
页码：781-800
DOI：10.1214/13-BA024
语种：English
出版社：International Society for Bayesian Analysis
摘要：We study a Bayesian model where we have made specific requests about the parameter values to be estimated. The aim is to find the parameter of a parametric family which minimizes a distance to the data generating density and then to estimate the discrepancy using nonparametric methods. We illustrate how coherent updating can proceed given that the standard Bayesian posterior from an unidentifiable model is inappropriate. Our updating is performed using Markov Chain Monte Carlo methods and in particular a novel method for dealing with intractable normalizing constants is required. Illustrations using synthetic data are provided.