The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

Probabilistic models are at the very core of modern machine learning (ML) and artificial
intelligence (AI). Indeed, probability theory provides a principled and almost universally …

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed
graph, in which terminal nodes represent probability distributions and non-terminal nodes …

We present a comprehensive survey of the advancements and techniques in the field of
tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies of depth functions in linear and metric …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies in linear and metric spaces, there is …

Sum-product networks are a relatively new and increasingly popular family of probabilistic
graphical models that allow for marginal inference with polynomial effort. They have been …

Probabilistic circuits are a class of probabilistic generative models that allow us to compute
different types of probabilistic queries in polynomial time. Unlike many of the mainstream …

Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive
gates are annotated by probability values. They allow for a compact representation of joint …

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed
graph, in which terminal nodes represent univariate probability distributions and non …