Learning 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.

References in corpus (4)

• Pathwise coordinate optimization
• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• On Graphical Models via Univariate Exponential Family Distributions
• Distributed Parameter Estimation via Pseudo-likelihood

Cited by in corpus (7)

• Structure estimation for mixed graphical models in high-dimensional data
• Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
• A General Framework for Mixed Graphical Models
• High-dimensional Mixed Graphical Models
• Selection and Estimation for Mixed Graphical Models
• High Dimensional Semiparametric Latent Graphical Model for Mixed Data
• Mixed and missing data: a unified treatment with latent graphical models