Marginalizing in Undirected Graph and Hypergraph Models
Artificial Intelligence
2013-02-01 v1
Abstract
Given an undirected graph G or hypergraph X model for a given set of variables V, we introduce two marginalization operators for obtaining the undirected graph GA or hypergraph HA associated with a given subset A c V such that the marginal distribution of A factorizes according to GA or HA, respectively. Finally, we illustrate the method by its application to some practical examples. With them we show that hypergraph models allow defining a finer factorization or performing a more precise conditional independence analysis than undirected graph models.
Keywords
Cite
@article{arxiv.1301.7366,
title = {Marginalizing in Undirected Graph and Hypergraph Models},
author = {Enrique F. Castillo and Juan Ferrándiz and Pilar Sanmartin},
journal= {arXiv preprint arXiv:1301.7366},
year = {2013}
}
Comments
Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)