Approximation Methods for Gaussian Process Regression

Abstract

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods. 

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@inproceedings{QuioneroCandela2007ApproximationMF, title={Approximation Methods for Gaussian Process Regression}, author={Joaquin Qui{\~n}onero-Candela and Carl Edward Ramussen and Christopher K. I. Williams}, year={2007} }