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Dirichlet process mixtures of generalised linear models
This video was recorded at 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia 2010. We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiasedness of the DP-GLM regression mean function estimate; we then give a practical example for when those conditions hold. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression including regression trees and Gaussian processes.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
