On exact Bayesian credible sets for discrete parameters

被引：0
|
作者：
Song, Chaegeun [1 ]
Li, Bing [1 ]
机构：
[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2025年 / 218卷
基金：
美国国家卫生研究院; 美国国家科学基金会;
关键词：
Bayesian classification; Highest posterior density set; Neyman-Pearson lemma; Pattern recognition; REGRESSION;
D O I：
10.1016/j.spl.2024.110295
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：
We introduce a generalized Bayesian credible set that can achieve any preassigned credible level, addressing a limitation of the current credible sets. This is achieved by exploiting a connection between the highest posterior density set and the Neyman-Pearson lemma.
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页数：5
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