文章基本信息

标题：Overall Objective Priors
本地全文：下载
作者：James O. Berger ; Jose M. Bernardo ; Dongchu Sun 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2015
卷号：10
期号：1
页码：189-221
DOI：10.1214/14-BA915
语种：English
出版社：International Society for Bayesian Analysis
摘要：In multi-parameter models, reference priors typically depend on the parameter or quantity of interest, and it is well known that this is necessary to produce objective posterior distributions with optimal properties. There are, however, many situations where one is simultaneously interested in all the parameters of the model or, more realistically, in functions of them that include aspects such as prediction, and it would then be useful to have a single objective prior that could safely be used to produce reasonable posterior inferences for all the quantities of interest. In this paper, we consider three methods for selecting a single objective prior and study, in a variety of problems including the multinomial problem, whether or not the resulting prior is a reasonable overall prior.