On Inclusion-Driven Learning of Bayesian Networks
Abstract
Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. Therefore, the search space of Bayesian network structures may be organized in equivalence classes, where each of them represents a different set of conditional independencies. The collection of sets of conditional independencies obeys a partial order, the so-called "inclusion order."
Cite
Text
Castelo and Kocka. "On Inclusion-Driven Learning of Bayesian Networks." Journal of Machine Learning Research, 2003.Markdown
[Castelo and Kocka. "On Inclusion-Driven Learning of Bayesian Networks." Journal of Machine Learning Research, 2003.](https://mlanthology.org/jmlr/2003/castelo2003jmlr-inclusiondriven/)BibTeX
@article{castelo2003jmlr-inclusiondriven,
title = {{On Inclusion-Driven Learning of Bayesian Networks}},
author = {Castelo, Robert and Kocka, Tomás},
journal = {Journal of Machine Learning Research},
year = {2003},
pages = {527-574},
volume = {4},
url = {https://mlanthology.org/jmlr/2003/castelo2003jmlr-inclusiondriven/}
}