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 …

Probabilistic model checking is a powerful technique for formally verifying quantitative
properties of systems that exhibit stochastic behaviour. Such systems are found in many …

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 …

This paper describes results from evaluating different self-localization approaches in indoor
environments for mobile robots. The algorithms examined are based on 2D laser scans and …

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

An increasingly used method for the engineering of software systems with strict quality-of-
service (QoS) requirements involves the synthesis and verification of probabilistic models for …

Markov models comprise states with probabilistic transitions. The analysis of these models is
ubiquitous and studied in, among others, reliability engineering, artificial intelligence …

We consider the problem of computing the satisfaction probability of a formula for stochastic
models with parametric uncertainty. We show that this satisfaction probability is a smooth …

We consider the problem of automatic control strategy synthesis, for discrete models of
robotic systems, to fulfill a task that requires reaching a goal state while obeying a given set …

This paper presents PAYNT, a tool to automatically synthesise probabilistic programs.
PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch …