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Abstract Details

Activity Number: 589
Type: Invited
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #303644
Title: Statistical Methods for Dynamic Spatio/Temporal Networks
Author(s): Adrian Dobra*+
Companies: University of Washington
Address: Department of Statistics, Seattle, WA, , United States
Keywords: Gaussian Graphical Models ; Gene expression ; Co-expression networks ; Tensor computations ; Bayesian analysis
Abstract:

In this talk we introduce a comprehensive Bayesian approach for model selection and inference in multi-way Gaussian graphical models which generalize vector-variate and matrix-variate Gaussian graphical models. We present a key application of our modeling framework to the determination of recurrent heavy subgraphs in gene expression networks. In addition to being computationally efficient, our methodology is able to average across many possible candidate subgraphs, thereby accounting for uncertainty in the membership of genes and experimental conditions.


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