Learning the Structure of Mixed Graphical Models

Jason D. Lee, Trevor J. Hastie

Research output: Contribution to journalArticle

53 Scopus citations

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 parameterization of the model. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)230-253
Number of pages24
JournalJournal of Computational and Graphical Statistics
Volume24
Issue number1
DOIs
StatePublished - Jan 2 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

Keywords

  • Group lasso
  • Lasso
  • Structure learning

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