Gaussian processes for regression


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
ISBN: 0262201070
Last Modified: 12 Feb 2024 08:00
Date Deposited: 15 Jul 2009 11:07
Full Text Link:
Related URLs: http://mitpress ... type=2&tid=8421 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1996-06
Authors: Williams, C. K. I.
Rasmussen, C. E.



Version: Published Version

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