Asymptotically optimal Bayesian sequential change detection and identification rules

Date
2013
Authors
Dayanik, S.
Powell, W. B.
Yamazaki, K.
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Source Title
Annals of Operations Research
Print ISSN
0254-5330
Electronic ISSN
1572-9338
Publisher
Volume
208
Issue
1
Pages
337 - 370
Language
English
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Abstract

We study the joint problem of sequential change detection and multiple hypothesis testing. Suppose that the common distribution of a sequence of i.i.d. random variables changes suddenly at some unobservable time to one of finitely many distinct alternatives, and one needs to both detect and identify the change at the earliest possible time. We propose computationally efficient sequential decision rules that are asymptotically either Bayes-optimal or optimal in a Bayesian fixed-error-probability formulation, as the unit detection delay cost or the misdiagnosis and false alarm probabilities go to zero, respectively. Numerical examples are provided to verify the asymptotic optimality and the speed of convergence. © 2012 Springer Science+Business Media, LLC.

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