Gaussian Processes for Machine Learning Toolbox 3.0

We are delighted to announce an updated release of the GPML Toolbox.

The code as well as the documentation and a tutorial can be obtained from
http://www.gaussianprocess.org/gpml/code

The GPML toolbox implements approximate inference algorithms for
Gaussian processes such as Expectation Propagation, the Laplace
Approximation and Variational Bayes for a wide variety of likelihood
functions for both regression and classification. It comes with a large
algebra of covariance and mean functions allowing for flexible
modeling.

Requirements: octave 3.2.x or matlab 7.x
Platform: any, tested on: mac, linux and windows
License: FreeBSD

Carl Edward Rasmussen & Hannes Nickisch