%0 Generic %1 lee2012learning %A Lee, Jason D. %A Hastie, Trevor J. %D 2012 %K logistic_regression pseudo-likelihood structure_learning %T Learning Mixed Graphical Models %U http://arxiv.org/abs/1205.5012 %X 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 parametrization of the model.

@misc{lee2012learning, 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 parametrization of the model.}, added-at = {2017-06-13T18:29:56.000+0200}, author = {Lee, Jason D. and Hastie, Trevor J.}, biburl = {https://www.bibsonomy.org/bibtex/266db8a78857f754bc405be650d66ea42/suqbar}, description = {1205.5012.pdf}, interhash = {586432490fe635a6091c800007a74ad4}, intrahash = {66db8a78857f754bc405be650d66ea42}, keywords = {logistic_regression pseudo-likelihood structure_learning}, note = {cite arxiv:1205.5012}, timestamp = {2017-06-13T18:29:56.000+0200}, title = {Learning Mixed Graphical Models}, url = {http://arxiv.org/abs/1205.5012}, year = 2012 }