Learning Bounded Tree-Width Bayesian Networks via Sampling

Siqi Nie, Cassio P. de Campos, Qiang Ji

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

7 Citations (Scopus)
194 Downloads (Pure)


Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.
Original languageEnglish
Title of host publicationSymbolic and Quantitative Approaches to Reasoning with Uncertainty
Subtitle of host publicationProceedings of 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015.
EditorsSébastien Destercke, Thierry Denoeux
PublisherSpringer International Publishing Switzerland
Number of pages10
ISBN (Electronic)978-3-319-20807-7
ISBN (Print) 978-3-319-20806-0
Publication statusPublished - 12 Jul 2015
Event13th European Conference, ECSQARU 2015 - Compiègne, France
Duration: 15 Jul 201517 Jul 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing


Conference13th European Conference, ECSQARU 2015

Bibliographical note

Blind peer reviewed by multiple reviewers.


  • Bayesian network
  • Structure learning
  • Bounded tree-width


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