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Bayesian regression and classification using mixtures of Gaussian processes
Author(s) -
Shi J.Q.,
MurraySmith R.,
Titterington D.M.
Publication year - 2003
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.744
Subject(s) - markov chain monte carlo , prior probability , bayesian probability , gaussian process , computer science , regression analysis , regression , artificial intelligence , mixture model , monte carlo method , kriging , machine learning , gaussian , bayesian inference , set (abstract data type) , data mining , statistics , mathematics , physics , quantum mechanics , programming language
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.