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Sparse Bayesian Nonparametric Regression

Sparse Bayesian Nonparametric Regression

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. One of the most common problems in machine learning and statistics consists of estimating the mean response X.beta from a vector of observations y assuming y=X.beta+epsilon where X is known, beta is a vector of parameters of interest and epsilon a vector of stochastic errors. We are particularly interested here in the case where the dimension K of beta is much higher than the dimension of y. We propose some flexible Bayesian models which can yield sparse estimates of beta. We show that as K tends to infinity, these models are closely related to a class of Levy processes. Simulations demonstrate that our models outperform significantly a range of popular alternatives.

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