Aggregation-based model reduction of a hidden Markov model

Kun Deng, Prashant Girdharilal Mehta, Sean P. Meyn

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


This paper is concerned with developing an information-theoretic framework to aggregate the state space of a Hidden Markov Model (HMM) on discrete state and observation spaces. The optimal aggregation is obtained by minimizing the Kullback-Leibler (K-L) divergence rate between joint laws describing the state and observation processes. The solution to this optimization problem is just the optimal aggregated Hidden Markov Model. This optimization problem is solved in two steps: The first step is to formulate the optimal solution for any fixed partition. The second step is to find the optimal partition by using an approximate dynamic programming framework. The algorithm can be implemented using a single sample path of the HMM and is illustrated with the aid of examples.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Number of pages6
StatePublished - 2010
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: Dec 15 2010Dec 17 2010


Other2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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