Abstract：Based on the principle of Bayesian information fusion and statistical inference, the dynamic evaluation model of uncertainty was established and the uncertainty of measurement results was updated in real time. The maximum entropy principle and hill-climbing algorithm were introduced to determine the prior distribution probability density function and the likelihood function of the sample information. The distribution of posterior distribution of PDF was calculated by combining the Bayes formula. And the optimal estimation of uncertainty was achieved. The case and simulation showed that the measurement uncertainty obtained by Bias and maximum entropy method was more accord with the standard requirement.
姜瑞,陈晓怀,王汉斌,肖颖,徐磊,程银宝,程真英. 基于贝叶斯信息融合的测量不确定度评定与实时更新[J]. 计量学报, 2017, 38(1): 123-126.
JIANG Rui,CHEN Xiao-huai,WANG Han-bin,XIAO Ying,XU Lei,CHENG Yin-bao,CHENG Zhen-ying. Evaluation and Real Time Updating of Measurement Uncertainty Based on Bayesian Information Fusion. Acta Metrologica Sinica, 2017, 38(1): 123-126.
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2017, Vol. 38 
