Learning Bounded Tree-Width Bayesian Networks via Sampling [chapter]

Siqi Nie, Cassio P. de Campos, Qiang Ji
2015 Lecture Notes in Computer Science  
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
more » ... iently 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.
doi:10.1007/978-3-319-20807-7_35 fatcat:fcxjip6vtjgqrdf445mh2hppdu