efficient state classification of finite state markov chains
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.
guided search verification model checking