Asymptotically optimal Bayesian sequential change detection and identification rules

Savas Dayanik (), Warren Powell () and Kazutoshi Yamazaki ()

Annals of Operations Research, 2013, vol. 208, issue 1, 337-370

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. Copyright Springer Science+Business Media, LLC 2013

Keywords: Sequential change detection and hypothesis testing; Asymptotic optimality; Optimal stopping (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10479-012-1121-6

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