Bayesian Regression and Classification Using Mixtures of Multiple Gaussian Processes
J. Q. Shi, R. Murray-Smith and D. M. Titterington
International Journal of Adaptive Control and Signal Processing Volume 17, Number 2, pp. 149-161, 2003.

Abstract

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 bymodelling observed Functional Electrical Stimulation experimental results. The classification model is illustrated on a synthetic example.

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