Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties

The ISO 98:1995 Guide to the expression of uncertainty in measurement (GUM) presents important application limitations. For its improvement, different supplements are being developed that will progressively enter into effect. The first of these supplements describes an alternative method for calcula...

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Bibliographic Details
Published in:Accreditation and quality assurance 2009-02, Vol.14 (2), p.95-106
Main Authors: SOLAGUREN-BEASCOA FERNANDEZ, M, CALDERON, J. M. Alegre, BRAVO DIEZ, P. M
Format: Article
Language:English
Subjects:
Analytical Chemistry
Applications programs
Biochemistry
Central limit theorem
Chemistry
Chemistry and Materials Science
Commercial Law
Confidence intervals
Ecotoxicology
Exact sciences and technology
Expected values
Food Science
Marketing
Matlab
Monte Carlo simulation
Practitioner's Report
Probability distribution
Propagation
Pseudorandom
Random numbers
Random variables
Software
Standard deviation
Statistical methods
Testing laboratories
Uncertainty
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Summary:The ISO 98:1995 Guide to the expression of uncertainty in measurement (GUM) presents important application limitations. For its improvement, different supplements are being developed that will progressively enter into effect. The first of these supplements describes an alternative method for calculating uncertainties, the Monte Carlo method (MCM), which is not restricted to the conditions of the method described in the GUM: the linearity of the model and the application of the central limit theorem. MCM requires computer calculation systems for generating pseudo-random numbers and for evaluating the model a large number of times. There are software applications that have been specifically developed for calculating uncertainties, some of which include MCM; but they do not allow the user to control all factors in the process, particularly the result stabilization criteria. On the contrary, its implementation in a mathematical program for general purposes such as MATLAB, enables total control over the process, is simple and benefits from its calculation speed. This article details programming in MATLAB for the implementation of the adaptive MCM method.
ISSN:0949-1775
1432-0517
DOI:10.1007/s00769-008-0475-6