A General Framework for Mixed Graphical Models

2 Nov 2014Eunho YangPradeep RavikumarGenevera I. AllenYulia BakerYing-Wooi WanZhandong Liu

"Mixed Data" comprising a large number of heterogeneous variables (e.g. count, binary, continuous, skewed continuous, among other data types) are prevalent in varied areas such as genomics and proteomics, imaging genetics, national security, social networking, and Internet advertising. There have been limited efforts at statistically modeling such mixed data jointly, in part because of the lack of computationally amenable multivariate distributions that can capture direct dependencies between such mixed variables of different types... (read more)

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