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Title: A Systematic Procedure for Assigning Uncertainties to Data Evaluations

Abstract

In this report, an algorithm that automatically constructs an uncertainty band around any evaluation curve is described. Given an evaluation curve and a corresponding set of experimental data points with x and y error bars, the algorithm expands a symmetric region around the evaluation curve until 68.3% of a set of points, randomly sampled from the experimental data, fall within the region. For a given evaluation curve, the region expanded in this way represents, by definition, a one-standard-deviation interval about the evaluation that accounts for the experimental data. The algorithm is tested against several benchmarks, and is shown to be well-behaved, even when there are large gaps in the available experimental data. The performance of the algorithm is assessed quantitatively using the tools of statistical-inference theory.

Authors:
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
902296
Report Number(s):
UCRL-TR-228283
TRN: US0702922
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; ALGORITHMS; BENCHMARKS; EVALUATION; PERFORMANCE

Citation Formats

Younes, W. A Systematic Procedure for Assigning Uncertainties to Data Evaluations. United States: N. p., 2007. Web. doi:10.2172/902296.
Younes, W. A Systematic Procedure for Assigning Uncertainties to Data Evaluations. United States. https://doi.org/10.2172/902296
Younes, W. 2007. "A Systematic Procedure for Assigning Uncertainties to Data Evaluations". United States. https://doi.org/10.2172/902296. https://www.osti.gov/servlets/purl/902296.
@article{osti_902296,
title = {A Systematic Procedure for Assigning Uncertainties to Data Evaluations},
author = {Younes, W},
abstractNote = {In this report, an algorithm that automatically constructs an uncertainty band around any evaluation curve is described. Given an evaluation curve and a corresponding set of experimental data points with x and y error bars, the algorithm expands a symmetric region around the evaluation curve until 68.3% of a set of points, randomly sampled from the experimental data, fall within the region. For a given evaluation curve, the region expanded in this way represents, by definition, a one-standard-deviation interval about the evaluation that accounts for the experimental data. The algorithm is tested against several benchmarks, and is shown to be well-behaved, even when there are large gaps in the available experimental data. The performance of the algorithm is assessed quantitatively using the tools of statistical-inference theory.},
doi = {10.2172/902296},
url = {https://www.osti.gov/biblio/902296}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 20 00:00:00 EST 2007},
month = {Tue Feb 20 00:00:00 EST 2007}
}