Statistical Model Checking

Given a stochastic system (a Markov Chain,...), the probabilistic model checking problem consists in deciding whether this system satisfies a property with a probability greater or equal to a certain threshold. There are various numerical algorithms for solving such problems. Unfortunately, they do not scale up to realistic systems. In this talk, we will show that techniques coming from the area of statistics can be used to solve the probabilistic model checking problem. Contrary to numerical algorithms, those statistic algorithms are applicable to realistic systems. In this talk, we will consider two families of such systems, namely digital/analog circuits and systems biology.


L'exposé sera en français.