Regression with Gaussian processes


The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out exactly using matrix operations. The method has been tested on two challenging problems and has produced excellent results.

Divisions: Aston University (General)
Additional Information: Copyright of Kluwer(now part of Springer). The original publication is available at
Uncontrolled Keywords: Bayesian analysis,complex form,integration,Gaussian process,matrix operations
ISBN: 978-0-7923-9933-9
Full Text Link:
Related URLs: http://www.spri ... detailsPage=toc (Publisher URL)
PURE Output Type: Chapter
Published Date: 1997
Authors: Williams, Christopher K. I.



Version: Accepted Version

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