Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).
NIST Authors in Bold
|Author(s):||Raghu N. Kacker; Blaza Toman; Ding Huang;|
|Title:||Comparison of ISO-GUM, draft GUM Supplement 1, and Bayesian statistics using simple linear calibration|
|Published:||January 01, 2006|
|Abstract:||We compare three approaches for quantifying uncertainty using a measurement equation: the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), draft GUM Supplement 1, and Bayesian statistics. We use the measurement equation for simple linear calibration as an illustration. It includes both TypeA and TypeB input variables. We consider three scenarios: (i) the measurement equation is linear. (ii) the measurement equation is non-linear and the TypeB input variables have normal distributions, and (iii) the measurement equation is non-linear and the Type B input variables have rectangular distributions. We consider both small and large uncertainties for the Type B input variables. We use each of the three approaches to quantify the uncertainty in measurement for each of the cases considered. Based on this study and the original publications, we discuss the merits and limitations of each approach.|
|Pages:||pp. S167 - S177|
|Keywords:||Bayesian statistics, linear calibration, standard uncertainty, uncertainty intervals, uncertainty in measurement|
|Research Areas:||Math, Software|
|PDF version:||Click here to retrieve PDF version of paper (177KB)|