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Contact
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Charles
Hagwood
Statistical Engineering
Division
Information Technology
Laboratory
301-975-2846
charles.hagwood@nist.gov
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Impetus/How Project Began
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During the past decade with increased computing power and new
research developments, Bayesian statistical methods have become
practical in diverse areas of statistical applications.
Bayesian methods provide a unified framework for optimally combining
information from multiple sources, resulting in simpler and improved
statistical analyses. Despite the widespread growth in Bayesian
methods, for the most the field of metrology has not taken advantage
of these methods. Both NIST researchers and their customers have
much to gain from these methods.
Recognizing the potential, NIST statisticians began exploring the
use of Bayesian methods in several metrological applications. After
some initial research, a five year competence initiative on Bayesian
metrology was started in FY99.
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Objective(s)
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Research, develop, and apply Bayesian methods to the
metrological problems of NIST; promulgate results to other metrology
laboratories and to NIST customers.
Four specific areas are targeted:
- traceability,
- interlaboratory comparisons,
- calibration, and
- part inspection.
These areas were chosen because of their importance to NIST and
their potential benefit from Bayesian methods.
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Staffing Profile and Funding
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FTE = 2.3 (Mathematical Statistician)
Funding is for $431K STRS (Competence)
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Timeline/
Milestones
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The timelines and milestones for this project are:
- FY99: Complete publication on a Bayesian model for
interlaboratory comparisons; explore the relationship between
the ISO uncertainty procedure and Bayesian statistics; present
review of Bayesian statistics to NIST staff.
- FY00: Learn applicable theory and methodology for Bayesian
hierchial model (Gaussian) models, acquire BUGS (Bayesian
computational software), and apply it to an example of NIST
data.
- FY 01: Apply hierarchical Bayes model to NIST
examples--Interlaboratory Proficiency Study. Formulate
Bayesian models for (Gaussian and non-Gaussian) SRM data, for
calibration data, part inspection plans; construct optimal
Bayesian designs for calibrations and for prototypical
interlaboratory comparison experiments.
- FY 02: Implement Bayesian hierarchical (Gaussian) analytic
methodology as web-product. Develop Bayesian model for Type
"B" uncertainty, and develop elicitation methodology and
software for modeling expert opinion. Define analyses for
canonical applications and continue to implement Bayesian
methodology as web-product in parallel to SEMATECH HANDBOOK
(for frequentist approach).
- FY 03: Develop or implement graphical representations of
Bayesian hierarchical models; Implement Bayesian methodology
and incorporate into standard NIST statistical practices.
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NIST Involvement
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The following NIST staff are involved in this project:
- Charles Hagwood (Division 898, ITL),
- Raghu Kacker (Division 898, ITL),
- Mark Vangel (Division 898, ITL),
- Christoph Witzgall (Division 891, ITL),
- Hung-kung Liu (Division 898, ITL),
- James Yen (Division 898, ITL),
- Nien-Fan Zhang (Division 898, ITL),
- Andrew Rukhin (Division 898, ITL and UMBC).
- Barry Taylor (Division 840, PL),
- Tyler Estler (Division 821, MEL),
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What Work Has ITL Done
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ITL has performed the following work for this project.
- Multiple publications on interlaboratory and key comparisons.
- Organization of a session on Bayesian Methods in Physical
Sciences at ASA 1999.
- Portion of NIST uncertainty course devoted to Bayesian
statistics.
- Within-project seminars on ISO uncertainty and Bayesian
statistics, BUGS (a software program for Bayesian computation),
Bayesian calibration and tolerance intervals, Bayesian
approach to SRM certification, and empirical Bayes methods.
- Initial work on a monograph on Bayesian metrology.
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What Work Has Been Done By Collaborators
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Tyler Estler and Steven Phillips (MEL) have researched and written
on the use of Bayesian methods in manufacturing.
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What Developments Have Occurred and How Has the Project Changed
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Nothing to report at this time.
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Publications or Artifacts
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This project has generated the following publications.
- "Equating Laboratories: Modeling and Analysis" by D.L. Banks
and K.R. Eberhardt.
- "Two-way Table as MANOVA in Interlaboratory Studies" by
A.L. Rukhin and M.G. Vangel.
- "Two-way Tables and Collaborative Studies" by M.G. Vangel
and A.Rukhin.
- "An ISO GUM Approach to Combining Results from Multiple
Methods" M.S. Levenson, D.L. Banks, K.R. Eberhardt, L.M. Gill,
W.F. Guthrie, H.K. Liu, M.G. Vangel, J.H. Yen, and N.F. Zhang.
- "An Interpretation of the Guide to the Expression of
Uncertainty du to Bias in Chemical Analysis", Ragu Kacker.
- "Uncertainty Concerning Key Comparison Reference Value:
Cryogenic Radiometers", Raju Datla, Jon Hardis, Ragu Kacker,
and Al Parr (under development).
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How Industries Have Benefited From NIST's Work
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NIST products and services, such as SRM's, calibrations and
key comparisons, will have higher quality uncertainty estimates.
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Acknowledgements of the ITL's Effort
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Nothing to report at this time.
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Future Related Activities
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Nothing to report at this time.
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Additional Information
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Additional information is available at
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