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.
Original language | English (US) |
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Pages (from-to) | 388-396 |
Number of pages | 9 |
Journal | Journal of Machine Learning Research |
Volume | 31 |
State | Published - Jan 1 2013 |
Externally published | Yes |
Event | 16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States Duration: Apr 29 2013 → May 1 2013 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence