Main Gaussian Process Software

We make software available for our research. Note that it is not necessarily 'production code', it is often just a snapshot of the software we used to produce the results in a particular paper. This makes it easier for other people to make comparisons and to reproduce our results. There are several software packages available from here, all associated with Gaussian Processes. They are mostly available on github and freely available under BSD-like licenses. Please cite us when you use our work.

Software Author Description

Python GPy Software on Github Python Toolbox GP, GP-LVM, Bayesian GP-LVM software and many other extentions from our group and range of collaborators.

MATLAB GPmat Software on Github MATLAB Toolbox IVM, GP and GP-LVM software and many other extentions from a range of collaborators.

C++ GPc Software on Github C++ Toolbox IVM, GP and GP-LVM software in C++.

R gprege Software on Github R Toolbox GP software in R.

Other Related Software

This software relies on the GPmat toolbox.

Software	Author	Description
Bayesian Fisher's Discriminant	Tonatiuh Pena Centeno	A Gaussian process interpretation of Kernel Fisher Discriminants.
Informative Vector Machine	Neil D. Lawrence	A sparse approximation to full Gaussian processes.
Multi-task Informative Vector Machine	Neil D. Lawrence	Multi-task learning with Gaussian processes using the IVM sparse approximation
Null category noise model	Neil D. Lawrence	A noise model for semi-supervised learning with Gaussian processes.
Probabilistic Point Assimilation	Nathaniel King and Neil D. Lawrence	A general fast variational method for GPs
GP Demos	Neil D. Lawrence	A set of Gaussian process demos, sampling from covariance functions etc..

Making Software Available

Really Reproducible Research in the Computational Sciences

I believe machine learning researchers should be making their software available at the same time they submit (or before) their papers to conference papers or journals, and I've carried out this practice since 2001. I wanted to put together the reasons why we should be doing this at some point, but it turns out that other researchers have already laid out reasons that pretty much match my own. So if you want to know why I (and why you should) make your code available that reproduces the figures in your papers please read this which was inspired by by ideas of Jon Claerbout. See his white paper here.

Thanks to Kevin Murphy for pointing out these papers.

Neil Lawrence, 05 December 2005

This document last modified Wednesday, 18-Jun-2014 09:30:48 UTC