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Learning Bounded Tree-Width Bayesian Networks via Sampling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9161)

Abstract

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.

Keywords

Bayesian network Structure learning Bounded tree-width 

Notes

Acknowledgements

This work is supported in part by the grant N00014-12-1-0868 from the US Office of Navy Research.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical, Computer and Systems EngineeringRensselaer Polytechnic InstituteTroyUSA
  2. 2.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastNorthern Ireland, UK

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