- Meeting ID: 867 6409 6440
- passcode: 149120
13h00 - 13h50 – Paolo Ballarini (MICS, CentraleSupélec)
A Bayesian approach to parametric verification of stochastic models
Complementary to the stochastic model checking problem is that of inference of a model’s parameters driven by the satisfaction of a target temporal behaviour. The goal in this case is to identify the regions of the parameter’s space that yield a positive probability to meet the target behaviour. By introducing the notion of satisfiability distance for basic time-bounded temporal modalities and by providing corresponding meter (hybrid) automata we adapted Approximate Bayesian Computation (ABC), a likelihood-free parameter-inference scheme, to solve the parametric stochastic model checking problem. In this talk I am going to give an overview of such automata-based adaptation of ABC schemes and will discuss some applications in biological modelling.
Dernière modification le 08/09/2023