Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/31763
Title: Gaussian process regression with multiple response variables
Authors: Wang, Bo
Chen, Tau
First Published: 2-Feb-2015
Publisher: Elsevier for Chemometrics Society
Citation: Chemometrics and Intelligent Laboratory Systems 142 (2015) 159–165
Abstract: Gaussian process regression (GPR) is a Bayesian non-parametric technology that has gained extensive application in data-based modelling of various systems, including those of interest to chemometrics. However, most GPR implementations model only a single response variable, due to the difficulty in the formulation of covariance function for correlated multiple response variables, which describes not only the correlation between data points, but also the correlation between responses. In the paper we propose a direct formulation of the covariance function for multi-response GPR, based on the idea that its covariance function is assumed to be the “nominal” uni-output covariance multiplied by the covariances between different outputs. The effectiveness of the proposed multi-response GPR method is illustrated through numerical examples and response surface modelling of a catalytic reaction process.
DOI Link: 10.1016/j.chemolab.2015.01.016
ISSN: 0169-7439
Links: http://www.sciencedirect.com/science/article/pii/S0169743915000180
http://hdl.handle.net/2381/31763
Embargo on file until: 2-Feb-2017
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Archived with reference to SHERPA/RoMEO and publisher website. NOTICE: this is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems Volume 142, 15 March 2015 DOI 10.1016/j.chemolab.2015.01.016
Appears in Collections:Published Articles, Dept. of Mathematics

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