Hierarchical Gaussian process mixtures for regression

Shi, J.Q., Murray-Smith, R. and Titterington, D.M. (2005) Hierarchical Gaussian process mixtures for regression. Statistics and Computing, 15(1), pp. 31-41. (doi: 10.1007/s11222-005-4787-7)



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


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.

Item Type:Articles
Keywords:Gaussian process; heterogeneity; hybrid Markov chain Monte Carlo; mixture models; nonparametric curve fitting
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Shi, J.Q., Murray-Smith, R., and Titterington, D.M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Statistics and Computing
Copyright Holders:Copyright © 2005 Springer
First Published:First published in Statistics and Computing 15(1):31-41
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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