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Journal of Automatic Chemistry
Volume 11, Issue 4, Pages 149-155

Calibration and measurement control based on Bayes statistics

1Computer and Automation Institute, Hungarian Academy of Sciences, PO Box 63, Budapest H1502, Hungary
2Institute of Forensic Science, PO Box 314/4, Budapest H1903, Hungary
3Institute of Information Theory and Automation Czechoslovak Academy of Sciences, Pod vodárenskou vezi 4, Praha 8 18208, Czech Republic

Copyright © 1989 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The Bayesian methodology described in this paper has the inherent capability of choosing, from calibration-type curves, candidates which are plausible with respect to measured data, expert knowledge and theoretical models (including the nature of the measurement errors). The basic steps of Bayesian calibration are reviewed and possible applications of the results are described in this paper. A calibration related to head-space gas chromatographic data is used as an example of the proposed method. The linear calibration case has been treated with a log-normal distributed measurement error. Such a treatment of noise stresses the importance of modelling the random constituents of any problem.