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