Abstract Details
Activity Number:
|
146
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 5, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #306968 |
Title:
|
Bayesian Clustering in Decomposable Graphs
|
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.