Gaussian Processes for Machine Learning (GPML) Toolbox 论文

详细信息

发表期刊/会议

Max Planck Institute for Plasma Physics

发表日期

2010-03-01

发表年份

2010

摘要

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 broad 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 inference, Expectation Propagation, Laplace‘s method and variational inference dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks. The package has a modular design, enabling simple addition of new functionality.