Structure learning of mixed graphical models

Jason D. Lee, Trevor J. Hastie

Research output: Contribution to journalConference article

13 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 is new and follows naturally from a particular parametrization of the model.

Original languageEnglish (US)
Pages (from-to)388-396
Number of pages9
JournalJournal of Machine Learning Research
Volume31
StatePublished - Jan 1 2013
Externally publishedYes
Event16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States
Duration: Apr 29 2013May 1 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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