The Gaussian Processes Web Site

This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes. Although Gaussian processes have a long history in the field of statistics, they seem to have been employed extensively only in niche areas. With the advent of kernel machines in the machine learning community, models based on Gaussian processes have become commonplace for problems of regression (kriging) and classification as well as a host of more specialized applications.

Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Chris Williams, the MIT Press, 2006, online version.

Statistical Interpolation of Spatial Data: Some Theory for Kriging, Michael L. Stein, Springer, 1999.

Statistics for Spatial Data (revised edition), Noel A. C. Cressie, Wiley, 1993

Spline Models for Observational Data, Grace Wahba, SIAM, 1990

The Bayesian Research Kitchen at The Wordsworth Hotel, Grasmere, Ambleside, Lake District, United Kingdom 05 - 07 September 2008.

A tutorial entitled Advances in Gaussian Processes on Dec. 4th at NIPS 2006 in VanCouver, slides, lecture.

The Gaussian Processes in Practice workshop at Bletchley Park, U.K., June 12-13 2006.

The Open Problems in Gaussian Processes for Machine Learning workshop at nips*05 in Whistler, December 10th, 2005.

The Gaussian Process Round Table meeting in Sheffield, June 9-10, 2005.

The kernel-machines web site.

Wikipedia entry on Gaussian processes.

The ai-geostats web site for spatial statistics and geostatistics.

The Bibliography of Gaussian Process Models in Dynamic Systems Modelling web site maintained by Juš Kocijan.

Andreas Geiger has written a simple Gaussian process regression Java applet, illustrating the behaviour of covariance functions and hyperparameters.

Other software that way be useful for implementing Gaussian process models:

The NETLAB package by Ian Nabney includes code for Gaussian process regression and many other useful thing, e.g. optimisers.

See Tom Minka's page on accelerating matlab and his lightspeed toolbox.

Matthias Seeger shares his code for Kernel Multiple Logistic Regression, Incomplete Cholesky Factorization and Low-rank Updates of Cholesky Factorizations.

See the software section of www.kernel-machines.org.

Below is a collection of papers relevant to learning in Gaussian process models. The papers are ordered according to topic, with occational papers occuring under multiple headings.

Tutorials

Regression

Classification

Covariance Functions and Properties of Gaussian Processes

Model Selection

Approximations

References from the Statistics Community

Consistency, Learning Curves and Bounds

Reproducing Kernel Hilbert Spaces

Reinforcement Learning

Gaussian Process Latent Variable Models (GP-LVM)

Applications

Other Topics