Quickest detection of Hidden Markov Models

Biao Chen, Peter Willett

Research output: Chapter in Book/Entry/PoemConference contribution

10 Scopus citations


Page's test is optimal in quickly detecting distributional changes among independent observations. In this paper we propose a similar procedure for the quickest detection of dependent signals which can be conveniently modeled as Hidden Markov Models. Considering Page's test as a repeated sequential probability ratio test (SPRT), we use Wald's approximation, with modification regarding the threshold overshoot, to predict the performance of the test, namely the average run length (ARL) between false alarms, T. Using the asymptotic convergence property of the test statistic, we are also able to predict the ARL to detection, D. Analysis shows T is asymptotically exponential in D, as in the i.i.d. case. The results are supported by numerical examples.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Editors Anon
PublisherIEEE Computer Society
Number of pages6
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997


OtherProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5)
CitySan Diego, CA, USA

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality


Dive into the research topics of 'Quickest detection of Hidden Markov Models'. Together they form a unique fingerprint.

Cite this