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The Markov chain Monte Carlo method: an approach to approximate counting and integration
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CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
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Electronic Notes in Theoretical Computer Science (ENTCS)
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Uniform random walks in very large models
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Electronic Notes in Theoretical Computer Science (ENTCS)
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FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
Non-redundant random generation algorithms for weighted context-free grammars
Theoretical Computer Science
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This paper presents some first results on how to perform uniform random walks (where every trace has the same probability to occur) in very large models. The models considered here are described in a succinct way as a set of communicating reactive modules. The method relies upon techniques for counting and drawing uniformly at random words in regular languages. Each module is considered as an automaton defining such a language. It is shown how it is possible to combine local uniform drawings of traces, and to obtain some global uniform random sampling, without construction of the global model.