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Gaussian Processes for Machine Learning (GPML) Toolbox

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84109

Nickisch,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rasmussen, C., & Nickisch, H. (2010). Gaussian Processes for Machine Learning (GPML) Toolbox. Journal of Machine Learning Research, 11, 3011-3015. Retrieved from http://jmlr.csail.mit.edu/papers/v11/rasmussen10a.html.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BD60-4
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.