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Stochastic Petri nets: an elementary introduction
Advances in Petri nets 1989
Bisimulation through probabilistic testing
Information and Computation
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Probabilistic non-determinism
Dynamical systems, measures, and fractals via domain theory
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Approximating labelled Markov processes
Information and Computation
Metrics for labelled Markov processes
Theoretical Computer Science - Logic, semantics and theory of programming
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Information and Computation - Special issue: Seventh workshop on coalgebraic methods in computer science 2004
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Electronic Notes in Theoretical Computer Science (ENTCS)
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Electronic Notes in Theoretical Computer Science (ENTCS)
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Electronic Notes in Theoretical Computer Science (ENTCS)
Bisimulation and cocongruence for probabilistic systems
Information and Computation - Special issue: Seventh workshop on coalgebraic methods in computer science 2004
An intrinsic characterization of approximate probabilistic bisimilarity
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Journal of the ACM (JACM)
Electronic Notes in Theoretical Computer Science (ENTCS)
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We study approximate reasoning about continuous-state labeled Markov processes. We show how to approximate a labeled Markov process by a family of finite-state labeled Markov chains. We show that the collection of labeled Markov processes carries a Polish space structure with a countable basis given by finite state Markov chains with rational probabilities. The primary technical tools that we develop to reach these results are: A finite-model theorem for the modal logic used to characterize bisimulation A categorical equivalence between the category of Markov processes (with simulation morphisms) with the w-continuous dcpo Proc , defined as the solution of the recursive domain equation Proc = ?Labels PProb ( Proc )1.The correspondence between labeled Markov processes and Proc yields logic complete for reasoning about simulation for continuous-state processes.