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2011 JMLR JMLR 2011

Dirichlet Process Mixtures of Generalized Linear Models

Abstract

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new class of methods for nonparametric regression. Given a data set of input-response pairs, the DP-GLM produces a global model of the joint distribution through a mixture of local generalized linear models. DP-GLMs allow both continuous and categorical inputs, and can model the same class of responses that can be modeled with a generalized linear model. We study the properties of the DP-GLM, and show why it provides better predictions and density estimates than existing Dirichlet process mixture regression models. We give conditions for weak consistency of the joint distribution and pointwise consistency of the regression estimate. [abs] [ pdf ][ bib ] © JMLR 2011. (edit, beta)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐣 Hot Topic Early Bird — density estimation
🐝 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, Robotics, Security & Privacy, Speech & Audio

Authors

Lauren A. Hannah , David M. Blei , Warren B. Powell

Topics

Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Regression Machine Learning > Optimization & Theory > Bayesian Inference

Keywords

dirichlet process density estimation nonparametric regression mixture model generalized linear model joint distribution
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