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Paper Detail

Paper IDE.1.1
Paper Title Optimal Two-Stage Bayesian Sequential Change Diagnosis
Authors Xiaochuan Ma, Lifeng Lai, University of California, Davis, United States; Shuguang Cui, the Chinese University of Hong Kong, Shenzhen, China
Session E.1: Detection Theory
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract In this paper, we formulate and solve a two-stage Bayesian sequential change diagnosis problem. Different from the one-stage sequential change diagnosis problem considered in the existing work, after a change has been detected, we can continue to collect samples so that we can identify the distribution after change more accurately. The goal is to minimize the total cost including delay, false alarm, and mis-diagnosis probabilities. We first convert the two-stage sequential change diagnosis problem into a two-ordered optimal stopping time problem. Using tools from multiple optimal stopping time problems, we obtain the optimal changed detection and distribution identification rules.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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