Analysis and comparison of Bayesian methods for measurement uncertainty evaluation
type: Item
instance of: scholarly article
title: Analysis and comparison of Bayesian methods for measurement uncertainty evaluation
zbMATH Open document ID: 1427.90301
DOI: 10.1155/2018/7509046
author: Yin-bao Cheng
author: Xiao-huai Chen
author: Zhen-ying Cheng
author: Rui Jiang
author: Hua-dong Fu
author: Hong-Li Li
author: Jing Lu
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Based on the Bayesian principle, the modern uncertainty evaluation methods can fully integrate prior and current sample information, determine the prior distribution according to historical information, and deduce the posterior distribution by integrating prior distribution and the current sample data with the Bayesian model. As such, it is possible to evaluate uncertainty, updating in real time the uncertainty of the measuring instrument according to regular measurement, and timely reflect the latest information on the accuracy of the measurement system. Based on the Bayesian information fusion and statistical inference principle, the model of uncertainty evaluation is established. The maximum entropy principle and the hill-climbing search optimization algorithm are introduced to determine the prior distribution probability density function and the sample information likelihood function. The probability density function of posterior distribution is obtained by the Bayesian formula to achieve the optimization estimation of uncertainty. Three methods of measurement uncertainty evaluation based on Bayesian analysis are introduced: the noninformative prior, the conjugate prior, and the maximum entropy prior distribution. The advantages and limitations of each method are discussed.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 68T35
zbMATH DE Number: 7019457
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7509046
OpenAlex ID: W2806192928
cites work: 10.1162/153244302760200696
Wikidata QID: Q129733403
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description: scientific article; zbMATH DE number 7019457