Learning the Structure of Mixed Graphical Models

Jason D. Lee, Trevor J. Hastie

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


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
Issue number1
StatePublished - Jan 2 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

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


  • Group lasso
  • Lasso
  • Structure learning


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