Regression with Gaussian processes

Christopher K. I. Williams

Research output: Chapter in Book/Published conference outputChapter

Abstract

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.
Original languageEnglish
Title of host publicationMathematics of neural networks
Subtitle of host publicationmodels, algorithms and applications
EditorsStephen W. Ellacott, John C. Mason, Iain J. Anderson
PublisherKluwer
Pages378-382
Number of pages5
ISBN (Print)978-0-7923-9933-9
Publication statusPublished - 1997

Publication series

NameOperations Research/Computer Science Interfaces Series
PublisherKluwer (Now part of Springer)
Number8

Bibliographical note

Copyright of Kluwer(now part of Springer). The original publication is available at www.springerlink.com

Keywords

  • Bayesian analysis
  • complex form
  • integration
  • Gaussian process
  • matrix operations

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