Learning Mixed Graphical Models

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. The structure and parameters of this model are learned using the pseudo-likelihood approximation with group-sparsity regularization. The pairwise model is also extended to incorporate features. Two algorithms for solving the resulting optimization problem are presented. The proposed models are compared with competing methods on synthetic data and a survey dataset.
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