Gaussian processes for regression

Williams, C. K. I. and Rasmussen, C. E. (1996). Gaussian processes for regression. IN: Advances in Neural Information Processing Systems 8. Touretzky, D. S.; Mozer, M. C. and Hasselmo, M. E. (eds) MIT.

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.

Divisions: Aston University (General)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Event Title: Advances in Neural Information Processing Systems 8
Event Type: Other
Event Dates: 1996-01-01 - 1996-01-01
Uncontrolled Keywords: Bayesian analysis,neural networks,Gaussian process,predictive,hyperparameters,matrix optimization,averaging,Hybrid Monte Carlo
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Related URLs: http://mitpress ... type=2&tid=8421 (Publisher URL)
Published Date: 1996-06
Authors: Williams, C. K. I.
Rasmussen, C. E.

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