Robustness in Bayesian nonparametrics

被引：1
|
作者：
Bose, Sudip [1 ]
机构：
[1] George Washington Univ, Dept Stat, Washington, DC 20052 USA
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2017年 / 82卷
关键词：
Dirichlet priors; Robustness; Optimality; Minimax posterior regret; Linear functionals; Ratio-linear quantities;
D O I：
10.1016/j.ijar.2016.12.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We consider Bayesian robustness in the context of Bayesian Nonparametrics, and specifically for the Dirichlet Process prior. We show how to find an optimal procedure, based on C-minimax posterior regret (CMPR) for a class of priors C. We consider regret based on squared error loss. The neighborhood classes considered are the density ratio (DR) class and the epsilon-contamination class. (C) 2016 Published by Elsevier Inc.
引用
收藏
页码：161 / 169
页数：9
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