Evaluation of Measurement Uncertainty Based on Bayesian Information Fusion

被引：0
|
作者：
Wang Shan [1 ]
Chen Xiaohuai [1 ]
Yang Qiao [1 ]
机构：
[1] Sch Instrument Sci & Optoelect Engn, Hefei 230009, Peoples R China
来源：
SIXTH INTERNATIONAL SYMPOSIUM ON PRECISION MECHANICAL MEASUREMENTS | 2013年 / 8916卷
关键词：
Bayesian Statistical Principle; information fusion; uncertainty assessment; simulation and verification;
D O I：
10.1117/12.2035725
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper raises a new method for evaluating uncertainty that taking count of both the record and the data. By using Bayesian Statistical Principle, the prior distribution and the posterior one, provided by the record and the data, were combined together. The statistical characteristics parameter estimation was descended from the posterior distribution, so that a formula of the uncertainty, which combined the advantages of type A and B, was acquired. By simulation and verification, this measurement shows great advantages compared with the others, especially to small size of data analysis.
引用
收藏
页数：8
相关论文
共 50 条
  • [1] A Bayesian approach to information fusion for evaluating the measurement uncertainty
    Sommer, Klaus-Dieter
    Kuehn, Olaf
    Leon, Fernando Puente
    Siebert, Bernd R. L.
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2009, 57 (03) : 339 - 344
  • [2] Research on measurement uncertainty evaluation methods based on Bayesian principle
    Chen, X.H.
    Cheng, Z.Y.
    Fei, Y.T.
    Key Engineering Materials, 2008, 381-382 : 583 - 586
  • [3] Optimisation estimation of uncertainty integrated with production information based on Bayesian fusion method
    Cheng, Yinbao
    Fu, Huadong
    Lyu, Jing
    Wang, Zhongyu
    Li, Hongli
    Chen, Xiaohuai
    JOURNAL OF ENGINEERING-JOE, 2019, 2019 (23): : 9178 - 9182
  • [4] Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling
    Honarmandi, P.
    Duong, T. C.
    Ghoreishi, S. F.
    Allaire, D.
    Arroyave, R.
    ACTA MATERIALIA, 2019, 164 : 636 - 647
  • [5] Evaluation of uncertainty visualization techniques for information fusion
    Riveiro, Maria
    2007 PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4, 2007, : 623 - 630
  • [6] Analysis and Comparison of Bayesian Methods for Measurement Uncertainty Evaluation
    Cheng, Yin-bao
    Chen, Xiao-huai
    Li, Hong-li
    Cheng, Zhen-ying
    Jiang, Rui
    Lu, Jing
    Fu, Hua-dong
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2018, 2018
  • [7] Uncertainty representation, quantification and evaluation for data and information fusion
    de Villiers, J. P.
    Laskey, K.
    Jousselme, A. -L.
    Blasch, E.
    de Waal, A.
    Pavlin, G.
    Costa, P.
    2015 18TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2015, : 50 - 57
  • [8] Guidance on Bayesian uncertainty evaluation for a class of GUM measurement models
    Demeyer, S.
    Fischer, N.
    Elster, C.
    METROLOGIA, 2021, 58 (01)
  • [9] Uncertainty measurement based on information fusion of three-source datasets: A granular computing viewpoint
    Wang, Pei
    Huang, Dan
    Li, Zhaowen
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 36 (02) : 1475 - 1490
  • [10] A BAYESIAN THEORY OF MEASUREMENT UNCERTAINTY
    WEISE, K
    WOGER, W
    MEASUREMENT SCIENCE AND TECHNOLOGY, 1993, 4 (01) : 1 - 11
12345