Structure Learning of Mixed Graphical Models


Jason Lee, Trevor Hastie ;
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:388-396, 2013.


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.

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