Learning Bayesian Networks with Thousands of Variables
Abstract
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints. Our approachis made of two parts. The first is a novel algorithm that effectively explores thespace of possible parent sets of a node. It guides the exploration towards themost promising parent sets on the basis of an approximated score function thatis computed in constant time. The second part is an improvement of an existingordering-based algorithm for structure optimization. The new algorithm provablyachieves a higher score compared to its original formulation. On very large datasets containing up to ten thousand nodes, our novel approach consistently outper-forms the state of the art.
Cite
Text
Scanagatta et al. "Learning Bayesian Networks with Thousands of Variables." Neural Information Processing Systems, 2015.Markdown
[Scanagatta et al. "Learning Bayesian Networks with Thousands of Variables." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/scanagatta2015neurips-learning/)BibTeX
@inproceedings{scanagatta2015neurips-learning,
title = {{Learning Bayesian Networks with Thousands of Variables}},
author = {Scanagatta, Mauro and de Campos, Cassio P and Corani, Giorgio and Zaffalon, Marco},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {1864-1872},
url = {https://mlanthology.org/neurips/2015/scanagatta2015neurips-learning/}
}