NIPS Proceedingsβ

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

Part of: Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Conference Event Type: Poster


We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.