Empirical Inference
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.
| Author(s): | Rasmussen, CE. and Nickisch, H. |
| Links: | |
| Journal: | Journal of Machine Learning Research |
| Volume: | 11 |
| Pages: | 3011-3015 |
| Year: | 2010 |
| Month: | November |
| Day: | 0 |
| BibTeX Type: | Article (article) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@article{6779,
title = {Gaussian Processes for Machine Learning (GPML) Toolbox},
journal = {Journal of Machine Learning Research},
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. },
volume = {11},
pages = {3011-3015},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = nov,
year = {2010},
author = {Rasmussen, CE. and Nickisch, H.},
month_numeric = {11}
}