Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference

  title={Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference},
  author={Dimitrios Milios and Guido Sanguinetti and David Schnoerr},
Probabilistic model checking for systems with large or unbounded state space is a challenging computational problem in formal modelling and its applications. Numerical algorithms require an explicit representation of the state space, while statistical approaches require a large number of samples to estimate the desired properties with high confidence. Here, we show how model checking of time-bounded path properties can be recast exactly as a Bayesian inference problem. In this novel formulation… CONTINUE READING
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Probabilistic model checking for continuous-time markov chains via sequential bayesian inference

  • D. Milios, G. Sanguinetti, D. Schnoerr
  • CoRR ArXiv, abs/1711.01863v2,
  • 2018
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