An information-theoretic framework to aggregate a markov chain

Kun Deng, Yu Sun, Prashant G. Mehta, Sean P. Meyn

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


This paper is concerned with an informationtheoretic framework to aggregate a large-scale Markov chain to obtain a reduced order Markov model. The Kullback- Leibler (K-L) divergence rate is employed as a metric to measure the distance between two stationary Markov chains. Model reduction is obtained by considering an optimization problem with respect to this metric. The solution is just the optimal aggregated Markov model. We show that the solution of the bi-partition problem is given by an eigenvalue problem. To construct a reduced order model with m super-states, a recursive algorithm is proposed and illustrated with examples.

Original languageEnglish (US)
Title of host publication2009 American Control Conference, ACC 2009
Number of pages6
StatePublished - 2009
Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
Duration: Jun 10 2009Jun 12 2009

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2009 American Control Conference, ACC 2009
Country/TerritoryUnited States
CitySt. Louis, MO

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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