skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Confidence Intervals from Realizations of Simulated Nuclear Data

Abstract

Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235U(n, f) and 239Pu (n, f) cross sections.

Authors:
 [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1399727
Report Number(s):
LLNL-TR-739175
DOE Contract Number:
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Citation Formats

Younes, W., Ratkiewicz, A., and Ressler, J. J.. Confidence Intervals from Realizations of Simulated Nuclear Data. United States: N. p., 2017. Web. doi:10.2172/1399727.
Younes, W., Ratkiewicz, A., & Ressler, J. J.. Confidence Intervals from Realizations of Simulated Nuclear Data. United States. doi:10.2172/1399727.
Younes, W., Ratkiewicz, A., and Ressler, J. J.. Thu . "Confidence Intervals from Realizations of Simulated Nuclear Data". United States. doi:10.2172/1399727. https://www.osti.gov/servlets/purl/1399727.
@article{osti_1399727,
title = {Confidence Intervals from Realizations of Simulated Nuclear Data},
author = {Younes, W. and Ratkiewicz, A. and Ressler, J. J.},
abstractNote = {Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235U(n, f) and 239Pu (n, f) cross sections.},
doi = {10.2172/1399727},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 28 00:00:00 EDT 2017},
month = {Thu Sep 28 00:00:00 EDT 2017}
}

Technical Report:

Save / Share: