Efficient state classification of finite state Markov chains

Authors:
Aiguo Xie;Peter A. Beerel
Affiliations:
Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA;Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA
Venue:
DAC '98 Proceedings of the 35th annual Design Automation Conference
Year:
1998

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Abstract

This paper presents an efficient method for state classification of finite state Markov chains using BDD-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 current state-of-the-art technique which requires the computation of the transitive closure of the transition relation of a Markov chain. Experiments in over a dozen synchronous and asynchronous systems demonstrate that our method dramatically reduces the CPU time needed, and solves much larger problems because of reduced memory requiremen ts.