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dc.contributor.authorDayanik, S.en_US
dc.contributor.authorPowell, W. B.en_US
dc.contributor.authorYamazaki, K.en_US
dc.date.accessioned2016-02-08T09:36:08Z
dc.date.available2016-02-08T09:36:08Z
dc.date.issued2013en_US
dc.identifier.issn0254-5330
dc.identifier.urihttp://hdl.handle.net/11693/20831
dc.description.abstractWe 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.en_US
dc.language.isoEnglishen_US
dc.source.titleAnnals of Operations Researchen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10479-012-1121-6en_US
dc.subjectAsymptotic optimalityen_US
dc.subjectOptimal stoppingen_US
dc.subjectSequential change detection and hypothesis testingen_US
dc.titleAsymptotically optimal Bayesian sequential change detection and identification rulesen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineering
dc.departmentDepartment of Mathematics
dc.citation.spage337en_US
dc.citation.epage370en_US
dc.citation.volumeNumber208en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1007/s10479-012-1121-6en_US
dc.identifier.eissn1572-9338


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