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Activity Number: 559
Type: Contributed
Date/Time: Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305139
Title: Gaussian Graphical Models Using Covariance Selection
Author(s): Rajesh Talluri*+ and Bani K. Mallick
Companies: Texas A&M University and Texas A&M University
Address: , College Station, TX, 77843,
Keywords: Graphical Models ; Covariance Selection ; Gene Networks
Abstract:

We try to develop Gaussian graphical models using covariance selection to model the relation between the variables. Estimating the zeros in the precision matrix is very vital to the modeling of graphs as a zero in the precision matrix signifies no relation between the two variables. We use this concept of covariance selection in three progressively complicated models to model gene networks or any general purpose networks using Bayesian Hierarchical Models. The three models are Shrinkage based Model, Selection Based Model and Multivariate finite mixture graph model.

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