Uncertainty in Artificial Intelligence
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Marginalizing in Undirected Graph and Hypergraph Models
Enrique Castillo, Juan Ferrándiz, Pilar Sanmartin
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:
Pages: 69-78
PS Link:
PDF Link: /papers/98/p69-castillo.pdf
BibTex:
@INPROCEEDINGS{Castillo98,
AUTHOR = "Enrique Castillo and Juan Ferrándiz and Pilar Sanmartin",
TITLE = "Marginalizing in Undirected Graph and Hypergraph Models",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "69--78"
}


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