Algorithms for Learning Decomposable Models and Chordal Graphs
Abstract
Decomposable dependency models and their graphical counterparts, i.e., chordal graphs, possess a number of interesting and useful properties. On the basis of two characterizations of decomposable models in terms of independence relationships, we develop an exact algorithm for recovering the chordal graphical representation of any given decomposable model. We also propose an algorithm for learning chordal approximations of dependency models isomorphic to general undirected graphs.
Cite
Text
de Campos and Huete. "Algorithms for Learning Decomposable Models and Chordal Graphs." Conference on Uncertainty in Artificial Intelligence, 1997.Markdown
[de Campos and Huete. "Algorithms for Learning Decomposable Models and Chordal Graphs." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/decampos1997uai-algorithms/)BibTeX
@inproceedings{decampos1997uai-algorithms,
title = {{Algorithms for Learning Decomposable Models and Chordal Graphs}},
author = {de Campos, Luis M. and Huete, Juan F.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {46-53},
url = {https://mlanthology.org/uai/1997/decampos1997uai-algorithms/}
}