PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mixed cumulative distribution networks
Ricardo Silva, Charles Blundell and Yee Whye Teh
In: AISTATS 2011, 11-13 Apr 2011, Florida, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8110
Deposited By:Yee Whye Teh
Deposited On:24 April 2011