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Bayesian linear regression and variable selection for spectroscopic calibration

This article is among the first to apply the Bayesian approach to multivariate chemometric calibration of spectroscopy. Methodologically it demonstrates that the ridge regression, well-known in the chemometric community, is a special case of Bayesian linear regression; subsequently a Bayesian variable selection approach is proposed that substantially improves the calibration accuracy. This work has seen significant follow-up in the literature.