Advances in learning Bayesian networks of bounded treewidth

Siqi Nie, Denis D. Mauá, Cassio P. de Campos, Qiang Ji

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)

Abstract

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014
PublisherCurran Associates
Pages2285-2293
Number of pages9
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014

Publication series

NameAdvances in Neural Information Processing Systems
Volume27
ISSN (Print)1049-5258

Conference

Conference28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014
CountryCanada
CityMontreal
Period8/12/1413/12/14

Bibliographical note

(top 4%, spotlight presentation, double-blind peer reviewed by >3 reviewers)

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