Edinburgh Research Explorer

Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish
Title of host publicationLearning in Graphical Models
Pages599-621
Number of pages23
ISBN (Electronic)978-94-011-5014-9
DOIs
Publication statusPublished - 1997

Publication series

NameNATO ASI Series D: Behavioural and Social Sciences
PublisherSpringer Netherlands
Volume89
ISSN (Print)0258-123X

Abstract

The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.

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