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Statistical Models and Computation to Evaluate Measurement Uncertainty



Antonio M. Possolo


In the course of the twenty years since the publication of the GUM, the recognition has been steadily growing, of the value that statistical models and statistical computing bring to the evaluation of measurement uncertainty, and of how they enable its probabilistic interpretation. These models and computational methods can address all the problems originally discussed and illustrated in the GUM, and enable addressing other problems, more challenging than those, that measurement science is facing today and that it is expected to face in the years ahead. These problems that lie beyond the reach of the techniques in the GUM include: (i) characterizing the uncertainty associated with the assignment of value to measurands of greater complexity than, or altogether different nature from, the scalar or vectorial quantities entertained in the GUM: for example, sequences of nucleotides in DNA, calibration functions and optical and other spectra, variability of radioactivity over a geographical region, shape of polymeric scaffolds for bioengineering applications, etc.; (ii) incorporating relevant information about the measurand that predates or is otherwise external to the measurement experiment; (iii) combining results from measurements of the same measurand that are mutually independent, obtained by different methods or produced by different laboratories. This review of several of these statistical models and computational methods, illustrates some of the advances that they have enabled, and in the process invites a reflection on the interesting historical fact that these very same models and methods, by and large were already available twenty years ago, when the GUM was first published --- but then the dialogue between metrologists, statisticians, and mathematicians, was still in bud, while it is in full bloom today, much to the benefit of all.


Measurement model, Observation equation, statistical model, nominal properties, laboratory effects, random effects, Bayesian methods, Gauss's formula, Measurement Uncertainty, Decibel, Calibration, Nonlinear least squares, Optimization.


Possolo, A. (2014), Statistical Models and Computation to Evaluate Measurement Uncertainty, Metrologia (Accessed July 15, 2024)


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Created July 11, 2014, Updated January 27, 2020