0000000001217894

AUTHOR

Pilar Sanmartin

showing 2 related works from this author

Potential approach in marginalizing Gibbs models

1999

Abstract Given an undirected graph G or hypergraph potential H model for a given set of variables V , we introduce two marginalization operators for obtaining the undirected graph G A or hypergraph H A associated with a given subset A ⊂ V such that the marginal distribution of A factorizes according to G A or H A , respectively. Finally, we illustrate the method by its application to some practical examples. With them we show that potential approach allow defining a finer factorization or performing a more precise conditional independence analysis than undirected graph models. Finally, we explain connections with related works.

Discrete mathematicsApplied MathematicsComparability graphStrength of a graphClique graphlaw.inventionTheoretical Computer ScienceCombinatoricslawGraph powerArtificial IntelligenceGibbs modelLine graphGraph (abstract data type)FactorizationNull graphMarginalizationRandom geometric graphHypergraph modelsSoftwareMathematicsInternational Journal of Approximate Reasoning
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Marginalizing in Undirected Graph and Hypergraph Models

2013

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.

FOS: Computer and information sciencesArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceComputer Science::Discrete Mathematics
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