Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report

Vaishak Belle, Guy Van den Broeck, Andrea Passerini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In recent years, there has been considerable progress on fast randomized algorithms that approximate probabilistic inference with tight tolerance and confidence guarantees. The idea here is to formulate inference as a counting task over an annotated propositional theory, called weighted model counting (WMC), which can be partitioned into smaller tasks using universal hashing. An inherent limitation of this approach, however, is that it only admits the inference of discrete probability distributions. In this work, we consider the problem of approximating inference tasks for a probability distribution defined over discrete and continuous random variables. Building on a notion called weighted model integration, which is a strict generalization of WMC and is based on annotating Boolean and arithmetic constraints, we show how probabilistic inference in hybrid domains can be put within reach of hashing-based WMC solvers. Empirical evaluations demonstrate the applicability and promise of the proposal.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016
PublisherIJCAI Inc
Number of pages5
ISBN (Print)978-1-57735-771-1
Publication statusPublished - 15 Jul 2016
EventTwenty-Fifth International Joint Conference on Artificial Intelligence - New York City, United States
Duration: 9 Jul 201615 Jul 2016


ConferenceTwenty-Fifth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2016
Country/TerritoryUnited States
CityNew York City
Internet address


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