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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Learning Bounded Tree-Width Bayesian Networks via Sampling
verfasst von : Siqi Nie, Cassio P. de Campos, Qiang Ji
Erschienen in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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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.