Gaussian Process Software
We make software available for our research. Note that it is not
'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. To download these software
packages you need to register, the packages are freely available for
academic use, you must seek a license for commercial use.
Follow instructions on the sites to access the software.
Links to Gaussian Process Software available on line
C++ Research Software
| Software | Author | Description |
| C++ GP-LVM | Neil D. Lawrence | GP-LVM software in C++. Currently doesn't implement the sparse algorithms, but includes dynamics and back constraints. |
| C++ IVM | Neil D. Lawrence | IVM Software in C++ , also includes the null category noise model for semi-supervised learning. |
MATLAB Research Software
Other Gaussian Process Software
This software relies on many of the toolboxes listed below.
MATLAB Toolkits
| Toolbox | Description |
| DATASETS | Various datasets and tools for loading them. |
| KERN | Various utilities for computing kernels. Includes contributions by many people. |
| NOISE | Various noise models for Gaussian processes. |
| NDLUTIL | Various utilities that some toolboxes rely on. |
| MLTOOLS | Various Machine Learning Tools that some toolboxes rely on. |
| MOCAP | Tools for loading in and playing with MOCAP data. |
| OPTIMI | Various optimisation tools. |
| PRIOR | Various utilities for prior distributions. |
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
ideas of Jon Claerbout. See his white paper
here.
Thanks to Kevin Murphy for pointing out these papers.
Neil Lawrence, 05 December 2005