Tasks such as social network analysis, human behavior recognition, or modeling
biochemical reactions, can be solved elegantly by using the probabilistic inference …

Weighted model counting (WMC) plays a central role in probabilistic reasoning. Given that
this problem is# P-hard, harder instances can generally only be addressed using …

Researchers in neuroscience have a growing number of datasets available to study the
brain, which is made possible by recent technological advances. Given the extent to which …

Given a formula in propositional or (finite-domain) first-order logic and some non-negative
weights, weighted model counting (WMC) is a function problem that asks to compute the …

Weighted model counting, that is, counting the weighted number of satisfying assignments of
a propositional formula, is an important tool in probabilistic reasoning. Recently, the use of …

Discrete integration in a high dimensional space of n variables poses fundamental
challenges. The WISH algorithm reduces the intractable discrete integration problem into n …

Researchers in neuroscience have a growing number of datasets available to study the
brain, which is made possible by recent technological advances. Given the extent to which …

A major goal of neuroscience research is to understand the circuits and patterns of neural
activity that give rise to mental processes and behavior. But a task of this complexity often …

Bayesian filtering (BF) is a general probabilistic framework for estimating the state of a
dynamic system that can be observed only indirectly thorough noisy measurements. This …

Title: An introduction to lifted probabilistic inference/edited by Guy van den Broeck, Kristian
Kersting, Sriraam Natarajan, and David Poole. Description: Cambridge, Massachusetts: The …