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Hierarchical Gaussian process mixtures for regression

Lookup NU author(s): Dr Jian Shi

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Abstract

As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic heterogeneity among the different replications, and the other is the requirement to invert a covariance matrix which is involved in the implementation of the model. The dimension of this matrix equals the sample size of the training data-set. In this paper, a Gaussian process mixture model for regression is proposed for dealing with the above two problems, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for its implementation. Application to a real data-set is reported. © 2005 Springer Science + Business Media, Inc.


Publication metadata

Author(s): Shi JQ, Murray-Smith R, Titterington DM

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2005

Volume: 15

Issue: 1

Pages: 31-41

Print publication date: 01/01/2005

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer

URL: http://dx.doi.org/10.1007/s11222-005-4787-7

DOI: 10.1007/s11222-005-4787-7


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