Distributed change detection in Gaussian graphical models

Chuanming Wei*, Ami Wiesel, Rick S. Blum

*Corresponding author for this work

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

6 Scopus citations

Abstract

This paper studies the distributed change detection problem in Gaussian graphical models (GGMs). Statistical analysis in GGM leads to several advantages, including a smaller number of parameters to model a large scale distribution, less samples required for the detection, faster detection and less communication costs. We formulate the hypothesis testing problem for change detection in GGMs and propose a global and centralized solution using the generalized likelihood ratio test (GLRT). We then provide two distributed approximations to this global test based on aggregation of multiple local or conditional tests. We compare the performance of these tests in the context of failure detection in smart grids.

Original languageAmerican English
Title of host publication2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
DOIs
StatePublished - 2012
Event2012 46th Annual Conference on Information Sciences and Systems, CISS 2012 - Princeton, NJ, United States
Duration: 21 Mar 201223 Mar 2012

Publication series

Name2012 46th Annual Conference on Information Sciences and Systems, CISS 2012

Conference

Conference2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
Country/TerritoryUnited States
CityPrinceton, NJ
Period21/03/1223/03/12

Keywords

  • Bartlett's test
  • Change detection
  • Gaussian graphical model
  • distributed signal processing

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