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 …

We present a novel static analysis technique to derive higher moments for program
variables for a large class of probabilistic loops with potentially uncountable state spaces …

We present Lilac, a separation logic for reasoning about probabilistic programs where
separating conjunction captures probabilistic independence. Inspired by an analogy with …

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

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 …

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 …

Computing schedulers that optimize reachability probabilities in MDPs is a standard
verification task. To address scalability concerns, we focus on MDPs that are compositionally …

This paper presents alternative techniques for inference on classical Bayesian networks in
which all probabilities are fixed, and for synthesis problems when conditional probability …

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 …

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 …