Forbes, A (2022) Sensitivity Analysis for Gaussian-Associated Features. Applied Sciences, 12 (6). 2808

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Abstract

This paper is concerned with the evaluation of the uncertainties associated with Gaussianassociated features following the GUM methodology. We show how sensitivity matrices necessary for a GUM uncertainty evaluation can be calculated and how the variance matrices associated with the feature parameters can be estimated for a range of complete and partial features common in engineering. Example results are given in tables that allow practitioners to estimate, a priori, the uncertainties associated with fitted parameters, given a proposed measurement strategy for the case in which the point-cloud variance matrix is a multiple of the identity matrix. The sensitivity matrices can be used to evaluate the uncertainties for associated features for more general point-cloud variance matrices. All the calculations involved are direct and involve no optimization or Monte Carlo sampling; they can be implemented in spreadsheet software, for example.

Item Type:	Article
Keywords:	coordinate metrology; Gaussian feature; uncertainty evaluation
Subjects:	Mathematics and Scientific Computing > Modelling
Divisions:	Data Science
Identification number/DOI:	10.3390/app12062808
Last Modified:	27 Oct 2022 13:49
URI:	http://eprintspublications.npl.co.uk/id/eprint/9556

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