Description:

The GPML toolbox implements approximate inference algorithms for Gaussian processes such as Expectation Propagation, the Laplace Approximation and Variational Bayes for a wide class of likelihood functions for both regression and classification. It comes with a big algebra of covariance and mean functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x.

Changes to previous version:

We now support inference on large datasets using the FITC approximation by Ed Snelson. The covariance function interface had to be slightly modified.

BibTeX Entry: Download

URL: Project Homepage

JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage

Supported Operating Systems: Agnostic, Platform Independent

Data Formats: Matlab, Octave

Tags: Classification, Regression, Approximate Inference, Gaussian Processes

Archive: download here

Other available revisons

Version	Changelog	Date
3.2	We now support inference on large datasets using the FITC approximation for non-Gaussian likelihoods for EP and Laplace's approximation. New likelihood functions: mixture likelihood, Poisson likelihood, label noise. We added two MCMC samplers.	January 21, 2013, 15:34:50
3.1	We now support inference on large datasets using the FITC approximation by Ed Snelson. The covariance function interface had to be slightly modified.	September 28, 2010, 05:51:56
3.0	Initial Announcement on mloss.org.	July 23, 2010, 12:13:58

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