Comparing probability distributions is an indispensable and ubiquitous task in machine
learning and statistics. The most common way to compare a pair of Borel probability …

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional
CG molecular mechanics force fields. We conclude that NN force fields are able to …

We consider the problems of statically refuting equivalence and similarity of output
distributions defined by a pair of probabilistic programs. Equivalence and similarity are two …

We prove results on the decidability and complexity of computing the total variation distance
(equivalently, the $ L_1 $-distance) of hidden Markov models (equivalently, labelled Markov …

Abstract Cyber-Physical Systems (CPSs) are integrations of networking and distributed
computing systems with physical processes, where feedback loops allow physical …

This work introduces a notion of approximate probabilistic trace equivalence for labelled
Markov chains, and relates this new concept to the known notion of approximate …

Abstractions of dynamical systems enable their verification and the design of feedback
controllers using simpler, usually discrete, models. In this paper, we propose a data-driven …

We introduce a general class of distances (metrics) between Markov chains, which are
based on linear behaviour. This class encompasses distances given topologically (such as …

Differential privacy is a widely studied notion of privacy for various models of computation,
based on measuring differences between probability distributions. We consider (epsilon …

Quantitative analysis of Markov models typically proceeds through numerical methods or
simulation-based evaluation. Since the state space of the models can often be large, exact …