Structure Learning of Mixed Graphical Models
Jason D. Lee, Trevor Hastie · 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 learn-ing. In previous work, authors have consid-ered structure learning of Gaussian graphi-cal models and structure learning of discrete models. Our approach is a natural general-ization of these two lines of work to the mixed case. The penalization scheme is new and fol-lows naturally from a particular parametriza-tion of the model. 1