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 …

Honeybees protect their colony against vertebrates by mass stinging and they coordinate
their actions during this crucial event thanks to an alarm pheromone carried directly on the …

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

We propose a method to optimise the parameters of a policy which will be used to safely
perform a given task in a data-efficient manner. We train a Gaussian process model to …

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 …

Abstract We present Jajapy, a Python library that implements a number of methods to aid the
modelling process of Markov models from a set of partially-observable executions of the …

Cyber-physical systems (CPS) are expected to continuously monitor the physical
components to autonomously calculate appropriate runtime reactions to deal with the …

We present a new method for statistical verification of quantitative properties over a partially
unknown system with actions, utilising a parameterised model (in this work, a parametric …

Stochastic population models are widely used to model phenomena in different areas such
as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond …

We present an approach that simultaneously infers model parameters while statistically
verifying properties of interest to chemical reaction networks, which we observe through data …