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Probabilistic Bisimulation: Naturally on Distributions

  • Holger Hermanns
  • Jan Krčál
  • Jan Křetínský
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8704)

Abstract

In contrast to the usual understanding of probabilistic systems as stochastic processes, recently these systems have also been regarded as transformers of probabilities. In this paper, we give a natural definition of strong bisimulation for probabilistic systems corresponding to this view that treats probability distributions as first-class citizens. Our definition applies in the same way to discrete systems as well as to systems with uncountable state and action spaces. Several examples demonstrate that our definition refines the understanding of behavioural equivalences of probabilistic systems. In particular, it solves a longstanding open problem concerning the representation of memoryless continuous time by memoryfull continuous time. Finally, we give algorithms for computing this bisimulation not only for finite but also for classes of uncountably infinite systems.

Keywords

Markov Decision Process Continuous Time Markov Chain Statistical Model Check Time Automaton Probabilistic Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Holger Hermanns
    • 1
  • Jan Krčál
    • 1
  • Jan Křetínský
    • 2
  1. 1.Saarland University – Computer ScienceSaarbrückenGermany
  2. 2.ISTAustria

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