Learning Bayesian Networks with Cops and Robbers
Abstract
Constraint-based methods for learning structures of Bayesian networks are based on testing conditional independencies between variables and constructing a structure that expresses the same conditional independencies as indicated by the tests. We present a constraint-based algorithm that learns the structure of a Bayesian network by simulating a cops-and-a-robber game. The algorithm is designed for learning structures of low treewidth distributions and in such case it conducts conditional independence tests only with small conditioning sets.
Cite
Text
Talvitie and Parviainen. "Learning Bayesian Networks with Cops and Robbers." Proceedings of pgm 2020, 2020.Markdown
[Talvitie and Parviainen. "Learning Bayesian Networks with Cops and Robbers." Proceedings of pgm 2020, 2020.](https://mlanthology.org/pgm/2020/talvitie2020pgm-learning/)BibTeX
@inproceedings{talvitie2020pgm-learning,
title = {{Learning Bayesian Networks with Cops and Robbers}},
author = {Talvitie, Topi and Parviainen, Pekka},
booktitle = {Proceedings of pgm 2020},
year = {2020},
pages = {473-484},
volume = {138},
url = {https://mlanthology.org/pgm/2020/talvitie2020pgm-learning/}
}