UNSPECIFIED (1998) Regression with input-dependent noise: A Gaussian process treatment. In: 11th Annual Conference on Neural Information Processing Systems (NIPS), DEC 01-06, 1997, DENVER, CO.

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Abstract

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software B Philosophy. Psychology. Religion > BF Psychology R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Series Name:	ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
Journal or Publication Title:	ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 10
Publisher:	M I T PRESS
ISBN:	0-262-10076-2
ISSN:	1049-5258
Editor:	Jordan, MI and Kearns, MJ and Solla, SA
Date:	1998
Volume:	10
Number of Pages:	7
Page Range:	pp. 493-499
Publication Status:	Published
Title of Event:	11th Annual Conference on Neural Information Processing Systems (NIPS)
Location of Event:	DENVER, CO
Date(s) of Event:	DEC 01-06, 1997
URI:	http://wrap.warwick.ac.uk/id/eprint/15030

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