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

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a
top public health priority. We describe EpiSimdemics-a scalable parallel algorithm to …

Given a parametric Markov model, we consider the problem of computing the rational
function expressing the probability of reaching a given set of states. To attack this principal …

Formal methods are increasingly being used for control and verification of dynamic systems
against complex specifications. In general, these methods rely on a relatively simple system …

This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of
labelled transition systems with exponentially delayed transitions. We show that IMCs are …

Counterexample-guided abstraction refinement (CEGAR) has been en vogue for the
automatic verification of very large systems in the past years. When trying to apply CEGAR to …

In the field of model checking, abstraction refinement has proved to be an extremely
successful methodology for combating the state-space explosion problem. However, little …

This paper studies the effect of bisimulation minimisation in model checking of monolithic
discrete-time and continuous-time Markov chains as well as variants thereof with rewards …

We study the problem of model checking Interval-valued Discrete-time Markov Chains
(IDTMC). IDTMCs are discrete-time finite Markov Chains for which the exact transition …

In this paper we present a novel abstraction technique for Markov decision processes
(MDPs), which are widely used for modelling systems that exhibit both probabilistic and …