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 …

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

This paper marries two state-of-the-art controller synthesis methods for partially observable
Markov decision processes (POMDPs), a prominent model in sequential decision making …

This paper presents PAYNT, a tool to automatically synthesise probabilistic programs.
PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch …

Markov decision processes are a ubiquitous formalism for modelling systems with non-
deterministic and probabilistic behavior. Verification of these models is subject to the famous …

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 …

In this work, we provide a deterministic alternative to the stochastic variational training of
generative autoencoders. We refer to these new generative autoencoders as AutoEncoders …

Probabilistic program synthesis consists in automatically creating programs generating
random values adhering to specified distributions. We consider here the family of …

The ability to provide succinct information about why a property does, or does not, hold in a
given system is a key feature in the context of formal verification and model checking. It can …

Markov decision processes (MDPs) provide a fundamental model for sequential decision
making under process uncertainty. A classical synthesis task is to compute for a given MDP …