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- W1840190952 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." @default.
- W1840190952 created "2016-06-24" @default.
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- W1840190952 date "2012-05-22" @default.
- W1840190952 modified "2023-09-27" @default.
- W1840190952 title "Learning Mixed Graphical Models" @default.
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