A Bayesian Approach to Sequential Change Detection and Isolation Problems

被引：1
|
作者：
Chen, Jie [1 ,2 ]
Zhang, Wenyi [1 ,2 ]
Poor, H. Vincent [3 ]
机构：
[1] Univ Sci & Technol China, CAS Key Lab Wireless Opt Commun, Hefei 230027, Peoples R China
[2] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China
[3] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2021年 / 67卷 / 03期
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Bayes methods; Delays; Upper bound; Change detection algorithms; Random variables; Stochastic systems; Probability distribution; Asymptotic behavior; average detection delay; Bayesian change detection; change detection and isolation; decision procedures;
D O I：
10.1109/TIT.2020.3042878
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The problem of sequential change detection and isolation under the Bayesian setting is investigated, where the change point is a random variable with a known distribution. A recursive algorithm is proposed, which utilizes the prior distribution of the change point. We show that the proposed decision procedure is guaranteed to control the false alarm probability and the false isolation probability separately under certain regularity conditions, and it is asymptotically optimal with respect to a Bayesian criterion.
引用
收藏
页码：1796 / 1803
页数：8
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