@inbook{49e34fc6bea447718002b6a8d91b6eb8,
title = "Learning Bounded Tree-Width Bayesian Networks via Sampling",
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",
author = "Siqi Nie and {de Campos}, {Cassio P.} and Qiang Ji",
note = "Blind peer reviewed by multiple reviewers.",
year = "2015",
month = jul,
day = "12",
doi = "10.1007/978-3-319-20807-7_35",
language = "English",
isbn = "978-3-319-20806-0",
volume = "9161",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "387--396",
booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty",
}