Importance Sampling for Stochastic Timed Automata

Abstract : We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise change of measure is then applied on-the-fly during simulations, ensuring that dead ends are never reached. The change of measure is guaranteed by construction to reduce the variance of the estimator with respect to crude Monte Carlo, while experimental results demonstrate that we can achieve substantial computational gains.
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Communication dans un congrès
Dependable Software Engineering: Theories, Tools, and Applications, Nov 2016, Beijing, China. 2016
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Soumis le : mardi 25 octobre 2016 - 13:17:44
Dernière modification le : mercredi 16 mai 2018 - 11:24:11

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Cyrille Jegourel, Kim Guldstrand Larsen, Axel Legay, Marius Mikučionis, Danny Poulsen, et al.. Importance Sampling for Stochastic Timed Automata. Dependable Software Engineering: Theories, Tools, and Applications, Nov 2016, Beijing, China. 2016. 〈hal-01387293〉

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