Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle

2008 ◽  
Vol 381-382 ◽  
pp. 583-586
Author(s):  
Xiao Huai Chen ◽  
Z.Y. Cheng ◽  
Ye Tai Fei

In current application of measurement uncertainty evaluation, dynamic uncertainty evaluation simply uses the static uncertainty methods. To change the situation, a new evaluation method of measurement uncertainty is investigated based on Bayesian principle in this paper. Bayesian evaluation method uses conjugate normal-inverted gamma distribution as the distribution function in uncertainty evaluation, which can be employed to evaluate both static and dynamic measurement uncertainty. The evaluation method put forward in this paper can achieve higher evaluation accuracy than the conventional methods, particularly in processing dynamic data with small samples. It has been proved in theory and by computer simulation.

2021 ◽  
Vol 18 ◽  
pp. 100274
Author(s):  
Mirosław Wojtyła ◽  
Paweł Rosner ◽  
Alistair B. Forbes ◽  
Enrico Savio ◽  
Alessandro Balsamo

2014 ◽  
Vol 14 (6) ◽  
pp. 308-316 ◽  
Author(s):  
Mingxiang Ling ◽  
Huimin Li ◽  
Qisheng Li

Abstract Measurement uncertainty evaluation based on the Monte Carlo method (MCM) with the assumption that all uncertainty sources are independent is common. For some measure problems, however, the correlation between input quantities is of great importance and even essential. The purpose of this paper is to provide an uncertainty evaluation method based on MCM that can handle correlated cases, especially for measurement in which uncertainty sources are correlated and submit to non-Gaussian distribution. In this method, a linear-nonlinear transformation technique was developed to generate correlated random variables sampling sequences with target prescribed marginal probability distribution and correlation coefficients. Measurement of the arm stretch of a precision centrifuge of 10-6 order was implemented by a high precision approach and associated uncertainty evaluation was carried out using the mentioned method and the method proposed in the Guide to the Expression of Uncertainty in Measurement (GUM). The obtained results were compared and discussed at last.


2015 ◽  
Vol 29 (1) ◽  
pp. 212-218 ◽  
Author(s):  
Dongxia Wang ◽  
Aiguo Song ◽  
Xiulan Wen ◽  
Youxiong Xu ◽  
Guifang Qiao

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