Comparison of Uncertainty Evaluation Method Based on Bayesian Principle
Journal Title: 河南科技大学学报（自然科学版） - Year 2016, Vol 37, Issue 6
Abstract
Aming at the limitation of the uncertainty evaluation only according to measuring sample information,the measurement uncertainty evaluation and updating method based on non-informative prior,conjugate prior and maximum entropy prior distribution were studied respectively by using the Bayesian information fusion theory. The evaluation process fully integrated the historical prior information with the current sample information,so that the reliability of the uncertainty evaluation was improved. The simulation examples show that the non-informative prior method does not integrate measurement data of each group and its simulation results fluctuates mostly. The conjugate prior method fluctuates greatly,and its simulation results gradually tend to theory value after multiple data fusion. The maximum entropy prior method fluctuates slightly and its simulation results gradually tend to theory value after data fusion.
Authors and Affiliations
Rui JIANG, Xiaohuai CHEN
Keywords
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- EP ID EP477369
- DOI 10.15926/j.cnki.issn1672-6871.2016.06.005
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