2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
