PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchical Gaussian process mixtures for regression
Jian Qing Shi, Roderick Murray-Smith and Mike Titterington
Statistics and Computing Volume 15, pp. 31-41, 2005.

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 elds. 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 dierent 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:310
Deposited By:Roderick Murray-Smith
Deposited On:02 December 2004