Mixed Cumulative Distribution Networks
Machine Learning
2010-09-01 v1 Machine Learning
Abstract
Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately there are currently no good parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.
Cite
@article{arxiv.1008.5386,
title = {Mixed Cumulative Distribution Networks},
author = {Ricardo Silva and Charles Blundell and Yee Whye Teh},
journal= {arXiv preprint arXiv:1008.5386},
year = {2010}
}
Comments
11 pages, 4 figures