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Efficient state classification of finite-state Markov chains

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2 Author(s)
Aiguo Xie ; Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA ; P. A. Beerel

This paper presents an efficient method for state classification of finite-state Markov chains using binary-decision diagram-based symbolic techniques. The method exploits the fundamental properties of a Markov chain and classifies the state space by iteratively applying reachability analysis. We compare our method with the state-of-the-art technique, which requires the transitive closure of the transition relation of a Markov chain. Experiments in over a dozen synchronous and asynchronous systems and queueing networks demonstrate that our method dramatically reduces the CPU time needed and solves much larger problems because of the reduced memory requirements

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  (Volume:17 ,  Issue: 12 )