Gaussian processes for regression

C. K. I. Williams, C. E. Rasmussen

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.
    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 8
    EditorsD. S. Touretzky, M. C. Mozer, M. E. Hasselmo
    PublisherMIT
    ISBN (Print)0262201070
    Publication statusPublished - Jun 1996
    EventAdvances in Neural Information Processing Systems 8 -
    Duration: 1 Jan 19961 Jan 1996

    Conference

    ConferenceAdvances in Neural Information Processing Systems 8
    Period1/01/961/01/96

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    Bibliographical note

    Copyright of the Massachusetts Institute of Technology Press (MIT Press)

    Keywords

    • Bayesian analysis
    • neural networks
    • Gaussian process
    • predictive
    • hyperparameters
    • matrix optimization
    • averaging
    • Hybrid Monte Carlo

    Cite this

    Williams, C. K. I., & Rasmussen, C. E. (1996). Gaussian processes for regression. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8 MIT.