An information-theoretic framework to aggregate a Markov chain

@article{Deng2009AnIF,
  title={An information-theoretic framework to aggregate a Markov chain},
  author={Kun Deng and Yu Sun and Prashant G. Mehta and Sean P. Meyn},
  journal={2009 American Control Conference},
  year={2009},
  pages={731-736}
}
This paper is concerned with an information-theoretic 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… CONTINUE READING

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