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 …

Abstract Model checking undiscounted reachability and expected-reward properties on
Markov decision processes (MDPs) is key for the verification of systems that act under …

Essential tasks for the verification of probabilistic programs include bounding expected
outcomes and proving termination in finite expected runtime. We contribute a simple yet …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

The verification problem in MDPs asks whether, for any policy resolving the nondeterminism,
the probability that something bad happens is bounded by some given threshold. This …

The de-facto standard approach in MDP verification is based on value iteration (VI). We
propose compositional VI, a framework for model checking compositional MDPs, that …

We investigate the problem of monitoring partially observable systems with nondeterministic
and probabilistic dynamics. In such systems, every state may be associated with a risk, eg …

We revisit two well-established verification techniques, k-induction and bounded model
checking (BMC), in the more general setting of fixed point theory over complete lattices. Our …

We present an exact Bayesian inference method for inferring posterior distributions encoded
by probabilistic programs featuring possibly unbounded loops. Our method is built on a …

We present version 2.0 of the Partial Exploration Tool (Pet), a tool for verification of
probabilistic systems. We extend the previous version by adding support for stochastic …