<XML><RECORDS><RECORD><REFERENCE_TYPE>0</REFERENCE_TYPE><REFNUM>6614</REFNUM><AUTHORS><AUTHOR>Shi,J.Q.</AUTHOR><AUTHOR>Murray-Smith,R.</AUTHOR><AUTHOR>Titterington,D.M.</AUTHOR></AUTHORS><YEAR>2003</YEAR><TITLE>Bayesian Regression and Classification Using Mixtures of Multiple Gaussian Processes</TITLE><PLACE_PUBLISHED> International Journal of Adaptive Control and Signal Processing, Volume 17, Number 3 </PLACE_PUBLISHED><PUBLISHER>Wiley</PUBLISHER><LABEL>Shi:2003:6614</LABEL><KEYWORDS><KEYWORD>Classification; Gaussian process; Heterogeneity; Hybrid Markov chain Monte Carlo; Mixture models; Nonlinear regression; Multiple</KEYWORD></KEYWORDS<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 by modelling observed Functional Electrical Stimulation experimental results. The classification model is illustrated on a synthetic example </ABSTRACT><NOTES>Special issue on MULTIPLE MODELS IN ADAPTIVE SYSTEMS</NOTES></RECORD></RECORDS></XML>