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.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v31-lee13a, title = {Structure Learning of Mixed Graphical Models}, author = {Lee, Jason and Hastie, Trevor}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {388--396}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/lee13a.pdf}, url = {http://proceedings.mlr.press/v31/lee13a.html}, 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.} }
Endnote
%0 Conference Paper %T Structure Learning of Mixed Graphical Models %A Jason Lee %A Trevor Hastie %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-lee13a %I PMLR %P 388--396 %U http://proceedings.mlr.press/v31/lee13a.html %V 31 %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 is new and follows naturally from a particular parametrization of the model.
RIS
TY - CPAPER TI - Structure Learning of Mixed Graphical Models AU - Jason Lee AU - Trevor Hastie BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-lee13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 388 EP - 396 L1 - http://proceedings.mlr.press/v31/lee13a.pdf UR - http://proceedings.mlr.press/v31/lee13a.html AB - 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. ER -
APA
Lee, J. & Hastie, T.. (2013). Structure Learning of Mixed Graphical Models. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:388-396 Available from http://proceedings.mlr.press/v31/lee13a.html.

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