Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Siqi Nie, Cassio P. de Campos, Qiang Ji

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

12 Citations (Scopus)
70 Downloads (Pure)


Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.
Original languageEnglish
Title of host publicationThe Thirtieth AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
Publication statusPublished - 2016
EventThe Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) - Phoenix Convention Center, Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

NameAAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399


ConferenceThe Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
Country/TerritoryUnited States
Internet address


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