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.
JIANG Rui,CHEN Xiao-huai,WANG Han-bin, et al. Evaluation and Real Time Updating of Measurement Uncertainty Based on Bayesian Information Fusion[J]. Acta Metrologica Sinica, 2017, 38(1): 123-126.
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