Bayesian clustering in decomposable graphs release_3ig4ygsamjciph6wcadj3plwie

by Luke Bornn, François Caron

Released as a article .

2012  

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.
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Type  article
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Date   2012-05-03
Version   v2
Language   en ?
arXiv  1005.5081v2
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