Bottom-up and Top-Down Uncertainty Quantification for Measurements

Burr, T.; Croft, S.; Favalli, A.; Krieger, T.; Weaver, B.

doi:10.1016/j.chemolab.2020.104224

Title: Bottom-up and Top-Down Uncertainty Quantification for Measurements

Journal Article · Thu Dec 24 00:00:00 EST 2020 · Chemometrics and Intelligent Laboratory Systems

DOI:https://doi.org/10.1016/j.chemolab.2020.104224· OSTI ID:1756810

Burr, T. ^[1]; Croft, S. ^[2]; Favalli, A. ^[1]; Krieger, T. ^[3]; Weaver, B. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Juelich (Germany)

Several recent papers address improved uncertainty quantification (UQ) for measurements used in nuclear safeguards. This paper reviews progress and presents new results for bottom-up (first principles) and top-down (empirical) UQ for safeguards, where the main quantitative measure of uncertainty is the total measurement error standard deviation (SD), which includes both random and systematic error components. The five main UQ topics addressed here include: (1) impact of making data-driven choices in SD estimation; (2) use of approximate Bayesian computation (ABC) for both bottom-up and top-down UQ; (3) computational calibration; (4) revisions to the guide to the expression of uncertainty in measurement (GUM), and (5) critique of a recently-suggested “Unified Theory of Measurement Errors and Uncertainties.”

View Accepted Manuscript (DOE)

View Accepted Manuscript (Publisher)

Cite

Export

Save

Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1756810

Alternate ID(s):: OSTI ID: 1776981

Report Number(s):: LA-UR-20-27332; TRN: US2205680

Journal Information:: Chemometrics and Intelligent Laboratory Systems, Vol. 211, Issue C; ISSN 0169-7439

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (18)

Uncertainty quantification for radiation measurements: Bottom-up error variance estimation using calibration information Burr, T.; Croft, S.; Krieger, T. Applied Radiation and Isotopes, Vol. 108 https://doi.org/10.1016/j.apradiso.2015.11.014	journal	February 2016
Estimation and Accuracy After Model Selection Efron, Bradley Journal of the American Statistical Association, Vol. 109, Issue 507 https://doi.org/10.1080/01621459.2013.823775	journal	July 2014
Dark uncertainty Thompson, Michael; Ellison, Stephen L. R. Accreditation and Quality Assurance, Vol. 16, Issue 10 https://doi.org/10.1007/s00769-011-0803-0	journal	July 2011
Revision of the ‘ Guide to the Expression of Uncertainty in Measurement ’. Why and how Bich, Walter Metrologia, Vol. 51, Issue 4 https://doi.org/10.1088/0026-1394/51/4/S155	journal	July 2014
Statistical evaluation of FRAM γ-ray isotopic analysis data Burr, T. L.; Sampson, T. E.; Vo, D. T. Applied Radiation and Isotopes, Vol. 62, Issue 6 https://doi.org/10.1016/j.apradiso.2005.01.002	journal	June 2005
Multiple-component radiation-measurement error models Burr, T. L.; Hemphill, G. S. Applied Radiation and Isotopes, Vol. 64, Issue 3 https://doi.org/10.1016/j.apradiso.2005.09.002	journal	March 2006
Comprehensive Uncertainty Quantification in Nuclear Safeguards Bonner, E.; Burr, T.; Krieger, T. Science and Technology of Nuclear Installations, Vol. 2017 https://doi.org/10.1155/2017/2679243	journal	January 2017
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation Blum, M. G. B.; Nunes, M. A.; Prangle, D. Statistical Science, Vol. 28, Issue 2 https://doi.org/10.1214/12-STS406	journal	May 2013
abctools: An R Package for Tuning Approximate Bayesian Computation Analyses Nunes, Matthew,A.; Prangle, Dennis The R Journal, Vol. 7, Issue 2 https://doi.org/10.32614/RJ-2015-030	journal	January 2015
A Nonlinear Approach to Linear Calibration Intervals Schechtman, Edna; Spiegelman, Cliff Journal of Quality Technology, Vol. 34, Issue 1 https://doi.org/10.1080/00224065.2002.11980130	journal	January 2002
The Effect of Ignoring Small Measurement Errors in Precision Instrument Calibration Carroll, Raymond J.; Spiegelman, Clifford H. Journal of Quality Technology, Vol. 18, Issue 3 https://doi.org/10.1080/00224065.1986.11979005	journal	July 1986
Analysis of calibration data for the uranium active neutron coincidence counting collar with attention to errors in the measured neutron coincidence rate Croft, Stephen; Burr, Tom; Favalli, Andrea Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 811 https://doi.org/10.1016/j.nima.2015.11.154	journal	March 2016
Uncertainty-based measurement quality control Huang, Hening Accreditation and Quality Assurance, Vol. 19, Issue 2 https://doi.org/10.1007/s00769-013-1032-5	journal	January 2014
Diagnostics for Nonlinearity and Heteroscedasticity in Errors-in-Variables Regression Carroll, R. J.; Spiegelman, C. H. Technometrics, Vol. 34, Issue 2 https://doi.org/10.1080/00401706.1992.10484907	journal	May 1992
A unified theory of measurement errors and uncertainties Huang, Hening Measurement Science and Technology, Vol. 29, Issue 12 https://doi.org/10.1088/1361-6501/aae50f	journal	October 2018
On use of Bayesian statistics to make the Guide to the Expression of Uncertainty in Measurement consistent Kacker, R.; Jones, A. Metrologia, Vol. 40, Issue 5 https://doi.org/10.1088/0026-1394/40/5/305	journal	September 2003
Improved uncertainty quantification in nondestructive assay for nonproliferation Burr, Tom; Croft, Stephen; Jarman, Ken Chemometrics and Intelligent Laboratory Systems, Vol. 159 https://doi.org/10.1016/j.chemolab.2016.10.007	journal	December 2016
Statistical Inference: The Big Picture Kass, Robert E. Statistical Science, Vol. 26, Issue 1 https://doi.org/10.1214/10-STS337	journal	February 2011

Similar Records

Uncertainty quantification for radiation measurements: Bottom-up error variance estimation using calibration information

Journal Article · Tue Nov 10 00:00:00 EST 2015 · Applied Radiation and Isotopes · OSTI ID:1756810

Burr, T.; Croft, S.; Krieger, T.; +3 more

Improved uncertainty quantification in nondestructive assay for nonproliferation

Journal Article · Thu Dec 01 00:00:00 EST 2016 · Chemometrics and Intelligent Laboratory Systems · OSTI ID:1756810

Burr, Tom; Croft, Stephen; Jarman, Ken; +3 more

Top-down versus bottom-up processing of influence diagrams in probabilistic analysis

Conference · Sun Jan 01 00:00:00 EST 1984 · OSTI ID:1756810

Timmerman, R D; Burns, T J; Dodds, Jr, H L

Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION
pproximate Bayesian computation (ABC)
bottom-up and top-down uncertainty quantification (UQ)
data-driven choices
guide to expression of uncertainty in measurement
item- specific bias
t-distributi

Title: Bottom-up and Top-Down Uncertainty Quantification for Measurements

Citation Formats

References (18)

Similar Records

Related Subjects