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Lookup NU author(s): Dr Jian Shi
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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.
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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