Randomization is a key element in sequential and distributed computing. Reasoning about
randomized algorithms is highly non-trivial. In the 1980s, this initiated first proof methods …

The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …

We launch the new probabilistic model checker S torm. It features the analysis of discrete-
and continuous-time variants of both Markov chains and MDPs. It supports the P rism and …

We present the probabilistic model checker Storm. Storm supports the analysis of discrete-
and continuous-time variants of both Markov chains and Markov decision processes. Storm …

We present PROPhESY, a tool for analyzing parametric Markov chains (MCs). It can
compute a rational function (ie, a fraction of two polynomials in the model parameters) for …

The formal analysis of critical systems is supported by a vast space of modelling formalisms
and tools. The variety of incompatible formats and tools however poses a significant …

We present an extensive collection of quantitative models to facilitate the development,
comparison, and benchmarking of new verification algorithms and tools. All models have a …

We propose a conceptually simple technique for verifying probabilistic models whose
transition probabilities are parametric. The key is to replace parametric transitions by …

Markov decision processes are widely used for planning and verification in settings that
combine controllable or adversarial choices with probabilistic behaviour. The standard …

Stochastic hybrid systems (SHS) are a rich mathematical modelling framework capable of
describing complex systems, where uncertainty and hybrid (that is, both continuous and …