2011
AISTATS
AISTATS 2011
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.
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Interdisciplinary Bridge
— Artificial Intelligence and Mathematics & Optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Keyword Pioneer
— acyclic directed mixed graph
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Hot Topic Early Bird
— latent variable
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Mathematics & Optimization > Mathematics > Graph Theory
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models