Learning mixed graphical models from data with p larger than n
Methodology
2012-02-20 v1 Machine Learning
Machine Learning
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
@article{arxiv.1202.3765,
title = {Learning mixed graphical models from data with p larger than n},
author = {Inma Tur and Robert Castelo},
journal= {arXiv preprint arXiv:1202.3765},
year = {2012}
}