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Bayesian regression and classification using mixtures of Gaussian processes

Lookup NU author(s): Dr Jian Shi

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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 (FES) experimental results. The classification model is illustrated in a synthetic example.

Publication metadata

Author(s): Shi JQ, Murray-Smith R, Titterington DM

Publication type: Article

Publication status: Published

Journal: International Journal of Adaptive Control and Signal Processing

Year: 2003

Volume: 17

Issue: 2

Pages: 149-161

Print publication date: 01/03/2003

ISSN (print): 0890-6327

ISSN (electronic): 1099-1115

Publisher: Wiley-Blackwell Publishing

URL: http://dx.doi.org/10.1002/acs.744

DOI: 10.1002/acs.744

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