Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
Artificial Intelligence
2016-05-12 v1
Abstract
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.
Cite
@article{arxiv.1605.03392,
title = {Learning Bounded Treewidth Bayesian Networks with Thousands of Variables},
author = {Mauro Scanagatta and Giorgio Corani and Cassio P. de Campos and Marco Zaffalon},
journal= {arXiv preprint arXiv:1605.03392},
year = {2016}
}