Bayesian Clustering in Decomposable Graphs

• Activity Number: 146
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Luke Bornn*+ and François Caron
• Companies: Department of Statistics, Harvard University and INRIA Bordeaux - Sud-Ouest
• Keywords: Decomposable Graphs ; Gaussian Graphical Models ; Prior Distribution ; Markov chain Monte Carlo ; Product Partition Model ; Product Graphical Model

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 are 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. Lastly, we explore American voting data, comparing the voting patterns amongst the states over the last century.