Bayesian clustering in decomposable graphs

Luke Bornn 1 François Caron 2, 3
2 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed on a particular prior distribution which derives its motivation from the class of product partition models; the properties of this prior relative to existing priors is examined through theory and simulation. We then demonstrate the use of graphical models in the field of agriculture, showing how the proposed prior distribution alleviates the inflexibility of previous approaches in properly modeling the interactions between the yield of different crop varieties.
Type de document :
Article dans une revue
Bayesian Analysis, International Society for Bayesian Analysis, 2011, 6 (4)
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Contributeur : Francois Caron <>
Soumis le : vendredi 28 mai 2010 - 09:26:27
Dernière modification le : jeudi 11 janvier 2018 - 06:22:36

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  • HAL Id : inria-00487168, version 1
  • ARXIV : 1005.5081



Luke Bornn, François Caron. Bayesian clustering in decomposable graphs. Bayesian Analysis, International Society for Bayesian Analysis, 2011, 6 (4). 〈inria-00487168〉



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