Learning treewidth-bounded Bayesian networks with thousands of variables

Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

31 Citations (Scopus)
867 Downloads (Pure)


We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

Original languageEnglish
Title of host publicationProceedings of the 30th Conference on Neutral Information Processing Systems, NIPS 2016
EditorsD. Lee, M. Sugiyama, U. Luxburg, I. Guyon, R. Garnett
PublisherNIPS Foundation, Inc
Number of pages9
ISBN (Electronic)9781510838819
Publication statusPublished - 12 Aug 2016
EventAnnual Conference on Neural Information Processing Systems 2016 - Centre Convencions Internacional Barcelona, Barcelona, Spain
Duration: 05 Dec 201610 Dec 2016

Publication series

NameAdvances in Neural Information Processing Systems


ConferenceAnnual Conference on Neural Information Processing Systems 2016
Abbreviated titleNIPS 2016
Internet address


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