Gaussian Processes for Machine Learning (GPML) Toolbox

2010

Article

ei


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.
Journal: Journal of Machine Learning Research
Volume: 11
Pages: 3011-3015
Year: 2010
Month: November
Day: 0

Department(s): Empirical Inference
Research Project(s): Probabilistic Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{6779,
  title = {Gaussian Processes for Machine Learning (GPML) Toolbox},
  author = {Rasmussen, CE. and Nickisch, H.},
  journal = {Journal of Machine Learning Research},
  volume = {11},
  pages = {3011-3015},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2010},
  month_numeric = {11}
}