Title
Computing Probabilistic Bisimilarity Distances via Policy Iteration.
Abstract
A transformation mapping a labelled Markov chain to a simple stochastic game is presented. In the resulting simple stochastic game, each vertex corresponds to a pair of states of the labelled Markov chain. The value of a vertex of the simple stochastic game is shown to be equal to the probabilistic bisimilarity distance, a notion due to Desharnais, Gupta, Jagadeesan and Panangaden, of the corresponding pair of states of the labelled Markov chain. Bacci, Bacci, Larsen and Mardare introduced an algorithm to compute the probabilistic bisimilarity distances for a labelled Markov chain. A modification of a basic version of their algorithm for a labelled Markov chain is shown to be the policy iteration algorithm applied to the corresponding simple stochastic game. Furthermore, it is shown that this algorithm takes exponential time in the worst case.
Year
Venue
Field
2016
CONCUR
Discrete mathematics,Markov chain mixing time,Combinatorics,Additive Markov chain,Continuous-time Markov chain,Markov model,Markov chain,Balance equation,Variable-order Markov model,Mathematics,Stochastic game
DocType
Citations 
PageRank 
Conference
5
0.73
References 
Authors
0
2
Name
Order
Citations
PageRank
Qiyi Tang174.49
Franck van Breugel252335.17