Sequential Detection and Isolation of a Correlated Pair

Anamitra Chaudhuri, Georgios Fellouris

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The problem of detecting and isolating a correlated pair among multiple Gaussian information sources is considered. It is assumed that there is at most one pair of correlated sources and that observations from all sources are acquired sequentially. The goal is to stop sampling as quickly as possible, declare upon stopping whether there is a correlated pair or not, and if yes, to identify it. Specifically, it is required to control explicitly the probabilities of three kinds of error: false alarm, missed detection, wrong identification. We propose a procedure that not only controls these error metrics, but also achieves the smallest possible average sample size, to a first-order approximation, as the target error rates go to 0. Finally, a simulation study is presented in which the proposed rule is compared with an alternative sequential testing procedure that controls the same error metrics.

Original languageEnglish (US)
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728164328
StatePublished - Jun 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: Jul 21 2020Jul 26 2020

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/TerritoryUnited States
CityLos Angeles

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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