Efficient Implementation of Gaussian Processes

M N Gibbs and David J C MacKay

Neural networks and Bayesian inference provide a useful framework within which to solve regression problems. However their parameterisation means that the Bayesian analysis of neural networks can be difficult. In this paper, we investigate a method for regression using Gaussian Process Priors which allows exact Bayesian analysis using matrix manipulation for fixed values of hyperparameters. We discuss in detail the workings of the method and we detail a range of mathematical and numerical techniques that are useful in applying Gaussian Processes to general problems including efficient approximate matrix inversion methods developed by Skilling.

gpros.ps.gz. | <- UK | Canada -> | gpros.ps.gz .

David MacKay's: home page, publications. bibtex file.