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Data-Driven Quickest Change Detection in Markov Models

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The problem of quickest change detection in Markov models is studied. A sequence of samples are generated from a Markov model, and at some unknown time, the transition kernel of the Markov model changes. The goal is to detect the change as soon as possible subject to false alarm constraints. The data-driven setting is investigated, where neither the pre-nor the post-change Markov transition kernel is known. A kernel based data-driven algorithm is developed, which applies to general state space and is recursive and computationally efficient. Performance bounds on the average running length and worst-case average detection delay are derived. Numerical results are provided to validate the performance of the proposed algorithm.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: Jun 4 2023Jun 10 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period06/4/2306/10/23

Keywords

  • Computationally Efficient
  • CuSum-Type Test
  • Maximum Mean Discrepancy
  • Second-Order Markov Chain
  • Sequential Change Detection

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