Scalable and reliable stochastic parametric verification with stochastic variational smoothed model checking

Parametric verification of linear temporal properties for stochastic models requires to compute the satisfaction probability of a certain behavioural property as a function of the parameters of the model. Smoothed model checking (smMC) [Bortolussi et al. (2016). Smoothed model checking for uncertain continuous-time Markov chains. Information and Computation, 247, 235–253] infers the satisfaction function over the entire parameter space from a limited set of observations obtained via simulation. As observations are costly and noisy, smMC leverages the power of Bayesian learning based on Gaussian Processes (GP), providing accurate reconstructions with statistical quantification of the uncertainty. In this paper, we propose Stochastic Variational Smoothed Model Checking (SV-smMC), which exploits stochastic variational inference (SVI) to approximate the posterior distribution of the smMC problem. The strength and flexibility of SVI, a stochastic gradient-based optimisation making inference easily parallelisable and enabling GPU acceleration, make SV-smMC applicable both to Gaussian Processes (GP) and Bayesian Neural Networks (BNN). SV-smMC extends the smMC framework by greatly improving scalability to higher dimensionality of parameter spaces and larger training datasets, thus overcoming the well-known limits of GP. Additionally, we combine the Bayesian quantification of uncertainty of SV-smMC with the Inductive Conformal Predictions framework to provide probabilistically approximately correct point-specific error estimates, with statistical guarantees over the coverage of the predictive error.