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Learning Bounded Treewidth Bayesian Networks

Elidan, Gal; Gould, Stephen

Description

With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the same time allow for tractable inference. While the method of thin junction trees can, in principle, be used for this purpose, its fully greedy nature makes it prone to overfitting, particularly when data is scarce. In this work we present a novel method for learning Bayesian networks of bounded treewidth that employs...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
URI: http://hdl.handle.net/1885/58335
Source: Journal of Machine Learning Research
DOI: 10.1.1.127.1023

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