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.
|Name||Lecture Notes in Computer Science|
|Publisher||Springer International Publishing|
|Conference||13th European Conference, ECSQARU 2015|
|Period||15/07/2015 → 17/07/2015|
Blind peer reviewed by multiple reviewers.
- Bayesian network
- Structure learning
- Bounded tree-width