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.