Gaussian Processes for Machine Learning (GPML) Toolbox
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.
Cite
Text
Rasmussen and Nickisch. "Gaussian Processes for Machine Learning (GPML) Toolbox." Machine Learning Open Source Software, 2010.Markdown
[Rasmussen and Nickisch. "Gaussian Processes for Machine Learning (GPML) Toolbox." Machine Learning Open Source Software, 2010.](https://mlanthology.org/mloss/2010/rasmussen2010jmlr-gaussian/)BibTeX
@article{rasmussen2010jmlr-gaussian,
title = {{Gaussian Processes for Machine Learning (GPML) Toolbox}},
author = {Rasmussen, Carl Edward and Nickisch, Hannes},
journal = {Machine Learning Open Source Software},
year = {2010},
pages = {3011-3015},
volume = {11},
url = {https://mlanthology.org/mloss/2010/rasmussen2010jmlr-gaussian/}
}