Marginalizing in Undirected Graph and Hypergraph Models

Castillo, Enrique F., Ferrándiz, Juan, Sanmartin, Pilar

arXiv.org Artificial Intelligence 

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.

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