This paper surveys the analysis of parametric Markov models whose transitions are labelled
with functions over a finite set of parameters. These models are symbolic representations of …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

We employ uncertain parametric CTMCs with parametric transition rates and a prior on the
parameter values. The prior encodes uncertainty about the actual transition rates, while the …

In spatially located, large scale systems, time and space dynamics interact and drives the
behaviour. Examples of such systems can be found in many smart city applications and …

Traffic systems, where human and autonomous drivers interact, are a very relevant instance
of complex systems and produce behaviors that can be regarded as trajectories over time …

Probabilistic model-checking aims to prove whether a Markov decision process (MDP)
satisfies a temporal logic specification. The underlying methods rely on an often unrealistic …

This article presents the complexity of reachability decision problems for parametric Markov
decision processes (pMDPs), an extension to Markov decision processes (MDPs) where …

We consider parametric Markov decision processes (pMDPs) that are augmented with
unknown probability distributions over parameter values. The problem is to compute the …

This paper studies parametric Markov decision processes (pMDPs), an extension to Markov
decision processes (MDPs) where transitions probabilities are described by polynomials …

We consider Markov decision processes (MDPs) in which the transition probabilities and
rewards belong to an uncertainty set parametrized by a collection of random variables. The …