Bottom-up and Top-Down Uncertainty Quantification for Measurements
- 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.”
- 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
Similar Records
Improved uncertainty quantification in nondestructive assay for nonproliferation
Top-down versus bottom-up processing of influence diagrams in probabilistic analysis