Detection and Estimation
E.1.4
Lecture
Detection Theory

Multislot and Multistream Quickest Change Detection in Statistically Periodic Processes

Taposh Banerjee, Gene Whipps, Prudhvi Gurram

Date & Time

01:00 am – 01:00 am

Abstract

Mixture-based algorithms are proposed for detecting a change in the distribution of a statistically periodic process in multislot and multistream settings. In the multislot change detection problem, the distribution of the observed process can change in any subset of time slots in each period. In the multistream change detection problem, there are parallel streams of observations, and the change can affect an arbitrary subset of streams. It is shown that the algorithms are asymptotically optimal in a Bayesian setting.


Presenters

Taposh Banerjee

University of Texas at San Antonio

Gene Whipps

CCDC Army Research Lab

Prudhvi Gurram

CCDC Army Research Lab and Booz Allen Hamilton
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Session Chair

Ali Tajer

Rensselaer Polytechnic Institute