An Inclusion Optimal Algorithm for Chain Graph Structure Learning
Abstract
This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.
Cite
Text
Peña et al. "An Inclusion Optimal Algorithm for Chain Graph Structure Learning." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Peña et al. "An Inclusion Optimal Algorithm for Chain Graph Structure Learning." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/pena2014aistats-inclusion/)BibTeX
@inproceedings{pena2014aistats-inclusion,
title = {{An Inclusion Optimal Algorithm for Chain Graph Structure Learning}},
author = {Peña, José M. and Sonntag, Dag and Nielsen, Jens Dalgaard},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {778-786},
url = {https://mlanthology.org/aistats/2014/pena2014aistats-inclusion/}
}