Theoretical Computer Science
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Analysing Biochemical Oscillation through Probabilistic Model Checking
Electronic Notes in Theoretical Computer Science (ENTCS)
Model Checking Timed and Stochastic Properties with CSL^{TA}
IEEE Transactions on Software Engineering
Efficient CTMC model checking of linear real-time objectives
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Parameter identification for Markov models of biochemical reactions
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Preface to the special issue on Probabilistic Model Checking
Formal Methods in System Design
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We consider continuous-time Markov chains (CTMC) with very large or infinite state spaces which are, for instance, used to model biological processes or to evaluate the performance of computer and communication networks. We propose a numerical integration algorithm to approximate the probability that a process conforms to a specification that belongs to a subclass of deterministic timed automata (DTAs). We combat the state space explosion problem by using a dynamic state space that contains only the most relevant states. In this way we avoid an explicit construction of the state-transition graph of the composition of the DTA and the CTMC. We also show how to maximize the probability of acceptance of the DTA for parametric CTMCs and substantiate the usefulness of our approach with experimental results from biological case studies.