Asymptotically optimal Bayesian sequential change detection and identification rules

Savas Dayanik, Warren Buckler Powell, Kazutoshi Yamazaki

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)337-370
Number of pages34
JournalAnnals of Operations Research
Volume208
Issue number1
DOIs
StatePublished - Sep 1 2013

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

Keywords

  • Asymptotic optimality
  • Optimal stopping
  • Sequential change detection and hypothesis testing

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