Abstract
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.