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Bayesian regression and classification using mixtures of Gaussian processes

For a large data set with groups of repeated measurements, a mixture model of Gaussian process priors is proposed for modelling the heterogeneity among the different replications. A hybrid Markov chain Monte‐Carlo (MCMC) algorithm is developed for the implementation of the model for regression and classification. The regression model and its implementation are illustrated by modelling observed functional electrical stimulation (FES) experimental results. The classification model is illustrated in a synthetic example. Copyright © 2003 John Wiley & Sons, Ltd.