文章基本信息

标题：Measuring Bayesian Robustness Using Rényi Divergence
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作者：Luai Al-Labadi
期刊名称：Stats
电子版ISSN：2571-905X
出版年度：2021
卷号：4
期号：2
页码：251-268
DOI：10.3390/stats4020018
出版社：MDPI AG
摘要：This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighborhood of the elicited prior are considered. The first one is the well-known ϵ-contaminated class, while the second one is the geometric mixing class. The proposed measure of robustness is based on computing the curvature of Rényi divergence between posterior distributions. Examples are used to illustrate the results by using simulated and real data sets.
关键词：Bayesian robustness; ϵ-contamination; geometric contamination; Rényi divergence Bayesian robustness ; ϵ-contamination ; geometric contamination ; Rényi divergence