2007
NIPS
NeurIPS 2007
Learning with Tree-Averaged Densities and Distributions
Abstract
We utilize the ensemble of trees framework, a tractable mixture over super- exponential number of tree-structured distributions [1], to develop a new model for multivariate density estimation. The model is based on a construction of tree- structured copulas – multivariate distributions with uniform on [0, 1] marginals. By averaging over all possible tree structures, the new model can approximate distributions with complex variable dependencies. We propose an EM algorithm to estimate the parameters for these tree-averaged models for both the real-valued and the categorical case. Based on the tree-averaged framework, we propose a new model for joint precipitation amounts data on networks of rain stations.
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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Machine Learning
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Trend Setter
— Clustering
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Keyword Pioneer
— tree-averaged densities
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— density estimation
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Statistical Learning
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Graphical Models