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Bayesian clustering in decomposable graphs
[article]
2012
arXiv
pre-print
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
arXiv:1005.5081v2
fatcat:3ig4ygsamjciph6wcadj3plwie