PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian Processes for Machine Learning (GPML) Toolbox
Carl Edward Rasmussen and Hannes Nickisch
Journal of Machine Learning Research Volume 11, pp. 3011-3015, 2010.

Abstract

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7469
Deposited By:Carl Edward Rasmussen
Deposited On:17 March 2011