Dataset Information

Learning the Structure of Mixed Graphical Models.

ABSTRACT: We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parametrization of the model. Supplementary materials for this paper are available online.

SUBMITTER: Lee JD

PROVIDER: S-EPMC4465824 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Learning the Structure of Mixed Graphical Models.

Lee Jason D JD Hastie Trevor J TJ

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20150101 1

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme ...[more]

PMID: 26085782

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Dataset Information

Learning the Structure of Mixed Graphical Models.

Publications

Learning the Structure of Mixed Graphical Models.

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