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Title: Comparisons Of Bayesian Approaches To Combining Results From Multiple Methods
Author: Hung-Kung Liu, Charles Hagwood, Nien Fan Zhang
Source: 2004 Measurement Science Conference
Year Published: 2004
Abstract: The problem of determining a consensus mean and its uncertainty from the results of multiple measurement methods or laboratories is an important NIST problem. Many solutions, both Bayesian and non- Bayesian, to this problem have been proposed over the years. In this paper, we propose an unied approach to some of the Bayes solutions by specifying the consensus mean as a measurable function of the method means and some ancillary variable. This Measurement Equation Approach conforms to the rules set in the ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM). When the measurement equation is linear in the ancillary variable, the uncertainty of our Bayes estimator has a decomposition that is ISO GUM compliant. We will also examine desirable criteria for an objective performance comparisons of the solutions to this problem, and use them to compare the existing solutions.

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