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Sparse Bayesian nonparametric regression

Abstract:

One of the most common problems in machine learning and statistics consists of estimating the mean response Xβ from a vector of observations y assuming y = Xβ + ε where X is known, β is a vector of parameters of interest and ε a vector of stochastic errors. We are particularly interested here in the case where the dimension K of β is much higher than the dimension of y. We propose some flexible Bayesian models which can yield sparse estimates of β. We show that as K → ∞ these models are close...

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Journal:
Proceedings of the 25th International Conference on Machine Learning
Pages:
88-95
Publication date:
2008-01-01
URN:
uuid:6377d960-1dd1-49d0-b47e-48ee0daa9ada
Source identifiers:
172742
Local pid:
pubs:172742
Language:
English

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