Probabilistic programming languages (PPLs) are an expressive means for creating and
reasoning about probabilistic models. Unfortunately hybrid probabilistic programs that …

Probabilistic circuits compute multilinear polynomials that represent probability distributions.
They are tractable models that support efficient marginal inference. However, various …

As illustrated by the success of integer linear programming, linear integer arithmetic is a
powerful tool for modelling combinatorial problems. Furthermore, the probabilistic extension …

Boolean circuits in d-DNNF form enable tractable probabilistic inference. However, as a key
insight of this work, we show that commonly used d-DNNF compilation approaches …

A probabilistic circuit (PC) succinctly expresses a function that represents a multivariate
probability distribution and, given sufficient structural properties of the circuit, supports …

Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for
many of today's probabilistic programming languages (PPLs), restricting their use as …