Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle

Xiao Huai Chen; Z.Y. Cheng; Ye Tai Fei

doi:10.4028/www.scientific.net/KEM.381-382.583

Paper Titles

Study on Scanning Squareness Measurement Method and Uncertainty Estimation
p.569

Improved Abbe Vertical Metroscope for the Calibration of Gauge Blocks
p.573

Analysis of Gear Measurement Using Virtual Gear Checker (VGC)
p.577

Industrial Robot Error Compensation Using Laser Tracker System
p.579

Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle
p.583

Uncertainty Estimation Using Monte-Carlo Method Constrained by Correlations of the Data
p.587

Analysis of Deformation and Stress Distribution of the Poly-Axial Pedicle Screw
p.591

Tooth Form Evaluation Using Ball Artifact Development of a Measuring Instrument of a Ball Center Distance Traceable to National Standard of Length
p.595

Using of Validated Software for Uncertainty Analyses Tools in Accredited Laboratories
p.599

HomeKey Engineering MaterialsKey Engineering Materials Vols. 381-382Research on Measurement Uncertainty Evaluation...

Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle

Abstract:

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.