Estimating Measurement Error Based on Evidence Theory

Ming Zhu Xiao

doi:10.4028/www.scientific.net/AMR.548.839

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 548Estimating Measurement Error Based on Evidence...

Estimating Measurement Error Based on Evidence Theory

Abstract:

Measurement error is traditionally represented with probability distributions. Although probabilistic representations of measurement error have been successfully employed in many analyses, such representations have been criticized for requiring more refined knowledge with respect to the existing error than that is really present. As a result, this paper proposes a general framework and process for estimating the measurement error based on evidence theory. In this research cumulative belief functions (CBFs) and cumulative plausibility functions (CPFs) are used to estimate measurement error. The estimation includes two steps:(1) modeling the parameters by means of a random set, and discrediting the random set to focal elements in finite numbers; (2)summarizing the propagation error. An example is demonstrated the estimation process.

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Periodical:

Advanced Materials Research (Volume 548)

Pages:

839-842

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.548.839

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Online since:

July 2012

Authors:

Ming Zhu Xiao

Keywords:

Belief Measure, Evidence Theory, Measurement Error, Plausibility Measure, Set Theory, Uncertainty

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