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Bayesian Approach to Assessing Uncertainty and Calculating a Reference Value in Key Comparison Experiments.

Blaza Toman1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-8980.

Journal of Research of the National Institute of Standards and Technology
|June 17, 2016
PubMed
Summary

A fully Bayesian approach offers a compatible and flexible method for estimating Reference Values in international Key Comparisons. This statistical strategy aligns with metrology practices and accommodates diverse experimental assumptions.

Keywords:
Bayesian hierarchical modelsMarkov Chain Monte Carlo methodslinear opinion poolsynthesis of probability distributions

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Area of Science:

  • Metrology
  • Statistical Science
  • Experimental Design

Background:

  • International Key Comparisons present statistical challenges in Reference Value estimation.
  • Existing methods for Reference Value estimation vary and are under international scrutiny.
  • Metrology relies on accurate and reliable estimation of key quantities.

Purpose of the Study:

  • To explore the suitability of a fully Bayesian approach for Reference Value estimation in Key Comparisons.
  • To demonstrate the compatibility of Bayesian methods with current metrology practices.
  • To show how Bayesian models can address the varied properties and assumptions inherent in these experiments.

Main Methods:

  • A fully Bayesian statistical framework is proposed.
  • The approach is evaluated for its compatibility with metrology principles.
  • Statistical models are developed to satisfy diverse experimental conditions.

Main Results:

  • The Bayesian approach is shown to be compatible with established metrology practices.
  • The proposed method can generate statistical models tailored to specific experimental needs.
  • Flexibility in model creation is a key advantage.

Conclusions:

  • A fully Bayesian approach provides a robust and adaptable solution for Reference Value estimation in Key Comparisons.
  • This methodology enhances the statistical rigor and practical applicability in metrology.
  • The Bayesian framework supports the development of sophisticated models for complex experimental data.