Learning Mixed Graphical Models from Data with P Larger than N
Abstract
Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data.
Cite
Text
Tur and Castelo. "Learning Mixed Graphical Models from Data with P Larger than N." Conference on Uncertainty in Artificial Intelligence, 2011.Markdown
[Tur and Castelo. "Learning Mixed Graphical Models from Data with P Larger than N." Conference on Uncertainty in Artificial Intelligence, 2011.](https://mlanthology.org/uai/2011/tur2011uai-learning/)BibTeX
@inproceedings{tur2011uai-learning,
title = {{Learning Mixed Graphical Models from Data with P Larger than N}},
author = {Tur, Inma and Castelo, Robert},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2011},
pages = {689-697},
url = {https://mlanthology.org/uai/2011/tur2011uai-learning/}
}