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Decentralized Quickest Change Detection in Hidden Markov Models for Sensor Networks

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Inference for Change-Point and Related Processes

The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors take observations from a hidden Markov model (HMM) and send sensor messages to a fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter $ heta$ in the HMM model changes from $ heta_0$ to $ heta_1$ at some unknown time. The problem is to determine the policies at the sensor and fusion center levels to jointly optimize the detection delay subject to the average run length (ARL) to false alarm constraint. The primary goal of this paper is to investigate how to choose the best binary stationary quantizers from the both theoretical and computational viewpoints when a CUSUM -type scheme is used at the fusion center. Further research is also given.

This talk is part of the Isaac Newton Institute Seminar Series series.

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