Prediction with Gaussian processes: from linear regression to linear prediction and beyond

Christopher K. I. Williams

    Research output: Chapter in Book/Published conference outputChapter

    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.
    Original languageEnglish
    Title of host publicationLearning in graphical models
    EditorsMichael Irwin Jordan
    PublisherMIT
    Pages599-621
    Number of pages23
    ISBN (Print)0262600323
    Publication statusPublished - 1999

    Keywords

    • Gaussian processes
    • Bayesian linear regression
    • hierarchical modelling
    • covariance functions
    • neural network

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