JSM 2005 - Toronto

Abstract #303615

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 56
Type: Topic Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303615
Title: Bayesian Covariance Selection
Author(s): Adrian Dobra*+
Companies: Duke University
Address: 5307 Rebecca Lynn Ln, Raleigh, NC, 27613, United States
Keywords: Bayesian inference ; Bayesian model averaging ; Bayesian networks ; Covariance selection ; Gene expression data ; Graphical models
Abstract:

We present a novel structural learning method called HdBCS that performs covariance selection in a Bayesian framework for datasets with tens of thousands of variables. HdBCS is based on the intrinsic connection between graphical models on undirected graphs and graphical models on directed acyclic graphs (Bayesian networks). We show how to produce and explore the corresponding association networks by Bayesian model averaging across the models identified. We illustrate the use of HdBCS with an example from a large-scale gene expression study of breast cancer. As time permits, we also will talk about extensions to graphical models with mixed variables.

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Revised March 2005