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.
arXiv.org Artificial Intelligence
Jan-30-2013
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- Europe > Spain (0.46)
- North America > United States (0.28)
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- Research Report (0.40)
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