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 …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

Markov decision processes (MDPs) are standard models for probabilistic systems with non-
deterministic behaviours. Long-run average rewards provide a mathematically elegant …

This paper is an introductory survey of available methods for the computation and
representation of probabilistic counterexamples for discrete-time Markov chains and …

Markov decision processes (MDPs) and continuous-time MDP (CTMDPs) are the
fundamental models for non-deterministic systems with probabilistic uncertainty. Mean …

Probabilistic model checking extends traditional model checking by incorporating
quantitative information about the probability of system transitions. However, probabilistic …

Compositional approaches to verification offer a powerful means to address the challenge of
scalability. In this paper, we develop techniques for compositional verification of probabilistic …

Providing compact and understandable counterexamples for violated system properties is
an essential task in model checking. Existing works on counterexamples for probabilistic …

For deterministic systems, a counterexample to a property can simply be an error trace,
whereas counterexamples in probabilistic systems are necessarily more complex. For …

Wireless networked control systems (WNCSs) with control loops closed over a wireless
network are prevailing these days. However, due to uncertainties such as random accessing …