Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers
Chen, T and Wang, B (2010) Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers Neurocomputing, 73 (13-15). 2718 - 2726. ISSN 0925-2312
|PDF - Accepted Version |
Available under License : See the attached licence file.
Official URL: http://dx.doi.org/10.1016/j.neucom.2010.04.014
Gaussianprocesses have received significant interest for statistical data analysis as a result of the good predictive performance and attractive analytical properties. When developing a Gaussianprocess regression model with a large number of covariates, the selection of the most informative variables is desired in terms of improved interpretability and prediction accuracy. This paper proposes a Bayesian method, implemented through the Markov chain Monte Carlo sampling, for variableselection. The methodology presented here is applied to the chemometriccalibration of near infrared spectrometers, and enhanced predictive performance and model interpretation are achieved when compared with benchmark regression method of partial least squares.
|Additional Information:||NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. 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 Neurocomputing, 73(13-15), August 2010, DOI 10.1016/j.neucom.2010.04.014.|
|Divisions:||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Deposited By:||Symplectic Elements|
|Deposited On:||23 May 2012 11:55|
|Last Modified:||13 Jun 2013 02:40|
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