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Network inference in matrix-variate Gaussian models with non-independent noise

Abstract:

Inferring a graphical model or network from observational data from a large number of variables is a well studied problem in machine learning and computational statistics. In this paper we consider a version of this problem that is relevant to the analysis of multiple phenotypes collected in genetic studies. In such datasets we expect correlations between phenotypes and between individuals. We model observations as a sum of two matrix normal variates such that the joint covariance function is...

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Iotchkova, V More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics, Clinical Medicine
Publication date:
2013-12-05
URN:
uuid:02120ed7-9c3a-4eb5-ba9c-8fc5f68869d2
Source identifiers:
441610
Local pid:
pubs:441610
Keywords:

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