Marginalizing in Undirected Graph and Hypergraph Models
Abstract
Given an undirected graph G or hypergraph H 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 ⊂ 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.
Cite
Text
Castillo et al. "Marginalizing in Undirected Graph and Hypergraph Models." Conference on Uncertainty in Artificial Intelligence, 1998.Markdown
[Castillo et al. "Marginalizing in Undirected Graph and Hypergraph Models." Conference on Uncertainty in Artificial Intelligence, 1998.](https://mlanthology.org/uai/1998/castillo1998uai-marginalizing/)BibTeX
@inproceedings{castillo1998uai-marginalizing,
title = {{Marginalizing in Undirected Graph and Hypergraph Models}},
author = {Castillo, Enrique F. and Fernández-Luna, Juan M. and Sanmartin, Pilar},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1998},
pages = {69-78},
url = {https://mlanthology.org/uai/1998/castillo1998uai-marginalizing/}
}