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 …

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 …

The analysis of formal models that include quantitative aspects such as timing or
probabilistic choices is performed by quantitative verification tools. Broad and mature tool …

This paper presents a novel method for the automated synthesis of probabilistic programs.
The starting point is a program sketch representing a finite family of finite-state Markov …

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

We revisit the symbolic verification of Markov chains with respect to finite horizon
reachability properties. The prevalent approach iteratively computes step-bounded state …

This paper proposes new analysis techniques for Bayes networks in which conditional
probability tables (CPTs) may contain symbolic variables. The key idea is to exploit scalable …

Randomization is a powerful technique to create robust controllers, in particular in partially
observable settings. The degrees of randomization have a significant impact on the system …

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