Mixed Cumulative Distribution Networks

Details Mixed Cumulative Distribution Networks Mixed Cumulative Distribution Networks

2010-08-31

arxiv.org/abs/1008.5386v1

Statistics

Machine Learning

11 pages, 4 figures

Scientific paper

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.

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