By P. Ciarlini, M. G. Cox, F. Pavese, G. B. Rossi
This quantity collects refereed contributions in keeping with the displays made on the 6th Workshop on complex Mathematical and Computational instruments in Metrology, held on the Istituto di Metrologia "G. Colonnetti" (IMGC), Torino, Italy, in September 2003. It offers a discussion board for metrologists, mathematicians and software program engineers that would motivate a more beneficial synthesis of talents, features and assets, and promotes collaboration within the context of european programmes, EUROMET and EA initiatives, and MRA standards. It comprises articles by means of an incredible, all over the world staff of metrologists and mathematicians focused on size technology and, including the 5 earlier volumes during this sequence, constitutes an authoritative resource for the mathematical, statistical and software program instruments essential to smooth metrology.
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Additional info for Advanced Mathematical and Computational Tools in Metrology VI
P > 1, it is necessary to define a measure M(X) as in (4). Rather than define ad hoc measures, we consider ones having a probabilistic justification. We make the further assumption about the model (1) that E N(O,V(X)). Then, given data y E Y ,the probability p(yla,A) that the data arose from parameters (a,A) is given by N and the log likelihood L(a,Xly) by The maximum likelihood (ML) estimates of a and X are determined by maximizing the probability p(yla,A) which is equivalent to minimizing -L(a, Xly) with respect to a and A.
The above described approach will now be examplified. 3. Example: Explosion limit determination The determination of explosion limits according to prEN 1839 is a semiqualitative testing procedure in the sense described here. The characteristic under investigation is the detonation of an potentially explosive gas mixture. It is assessed by a pattern recognition-like procedure based on the decision whether a flame separation or the formation of a tall aureole took place following ignition. The result is a pasdfail decision.
A model-based least-squares method is introduced that compensates for these effects. It incorporates correction parameters to account for the systematic uncertainty components, thus eliminating the correlation effects and reducing the calibration model uncertainty. Results are presented for calibration data modelled by straight-line functions. Generalizations are indicated. 1. l. Measurement process N samples, each from a different standard, that consist of the same q gas components, are injected in turn to a gas chromatograph (GC) (figure 1).
Advanced Mathematical and Computational Tools in Metrology VI by P. Ciarlini, M. G. Cox, F. Pavese, G. B. Rossi