Importance Sampling for Model Checking of Continuous Time Markov Chains

Abstract : Model checking real time properties on probabilistic systems requires computing transient probabilities on continuous time Markov chains. Beyond numerical analysis ability, a probabilistic framing can only be obtained using simulation. This statistical approach fails when directly applied to the estimation of very small probabilities. Here combining the uniformization technique and extending our previous results, we design a method which applies to continuous time Markov chains and formulas of a timed temporal logic. The corresponding algorithm has been implemented in our tool cosmos. We present experimentations on a relevant system, with drastic time reductions with respect to standard statistical model checking.