A minimum entropy criterion for distribution selection for measurement uncertainty analysis

This paper presents a minimum entropy criterion for selecting the best probability distribution among a set of candidate distributions based on available information for measurement uncertainty analysis. We consider two cases that are most commonly encountered in practice: A and B. In Case A, the av...

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Bibliographic Details
Published in:Measurement science & technology 2024-03, Vol.35 (3), p.35014
Main Author: Huang, Hening
Format: Article
Language:English
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Summary:This paper presents a minimum entropy criterion for selecting the best probability distribution among a set of candidate distributions based on available information for measurement uncertainty analysis. We consider two cases that are most commonly encountered in practice: A and B. In Case A, the available information is a series of observations. In Case B, the available information is the maximum permissible error according to manufacturer’s specification. Three candidate distributions are considered in Case A: the scaled and shifted z -distribution (i.e. normal distribution), the scaled and shifted t -distribution, and the Laplace distribution. Five candidate distributions are considered in Case B: rectangular, triangular, quadratic, raised cosine, and half-cosine. According to the proposed minimum entropy criterion, the scaled and shifted z -distribution is the best distribution in Case A, and the raised cosine distribution is the best distribution in Case B. A case study is presented to demonstrate the effectiveness of the proposed minimum entropy criterion.
ISSN:0957-0233
1361-6501
DOI:10.1088/1361-6501/ad1476