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). pp. 2718-2726.
tchen10-neucomp.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
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.
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Identification Number :||https://doi.org/10.1016/j.neucom.2010.04.014|
|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.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||23 May 2012 10:55|
|Last Modified :||23 Sep 2013 19:13|
Actions (login required)
Downloads per month over past year