Mixed Cumulative Distribution Networks
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 parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.
Cite
Text
Silva et al. "Mixed Cumulative Distribution Networks." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Silva et al. "Mixed Cumulative Distribution Networks." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/silva2011aistats-mixed/)BibTeX
@inproceedings{silva2011aistats-mixed,
title = {{Mixed Cumulative Distribution Networks}},
author = {Silva, Ricardo and Blundell, Charles and Teh, Yee Whye},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {670-678},
volume = {15},
url = {https://mlanthology.org/aistats/2011/silva2011aistats-mixed/}
}