Abstract： Maximum entropy is one of the chief methods in the evaluation of measurement uncertainty based on probability distribution, and the higher moments it depended on a larger sample of measurement data. However, measurement in calibration and testing laboratories is generally a small sample survey, so evaluation of measurement uncertainty based on the maximum entropy method for small samples lacks a certain degree of reliability. A method for uncertainty evaluation based on quantile function and probability weighting moment is put forward as the constraint condition of maximum information entropy method. By this way, the high order moment is fell down for a moment, the probability distribution is solved with the combination of genetic algorithm, and the complicated calculation problem arising from quantile interval estimation of asymmetric distribution is managed about with Bootstrap distribution estimation for expanded uncertainty and coverage interval.
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2014, Vol. 35 
