Mixed Cumulative Distribution Networks

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Ricardo Silva, Charles Blundell, Yee Whye Teh ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:670-678, 2011.

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. [pdf][supplementary]

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