Gaussian processes for regression

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

Research output: Chapter in Book/Published conference outputChapter

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

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

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