Gaussian Processes for Regression

Williams, CKI; Rasmussen, CE

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Gaussian Processes for Regression

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://papers.nips.cc/paper/1048-gaussian-processes-for-regression.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Williams, C., & Rasmussen, C. (1996). Gaussian Processes for Regression. In D. Touretzky, M. Mozer, & M. hasselmo (Eds.), Advances in Neural Processing Systems 8 (pp. 514-520). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-EB66-9

Abstract

The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits 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.