Sparse Bayesian nonparametric regression

Journal article

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 closely related to a class of Levy processes. Simulations demonstrate that our models outperform significantly a range of popular alternatives. Copyright 2008 by the author(s)/owner(s).

Authors: Caron, F; Doucet, A

• Journal: Proceedings of the 25th International Conference on Machine Learning
• Pages: 88-95
• Publication date: 2008-01-01
• DOI: 10.1145/1390156.1390168
• Language: English