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Effect of Epistemic Uncertainty Modeling Approach on Decision-Making [electronic resource] : Example using Equipment Performance Indicator

Published:
Washington, D.C. : United States. Office of the Assistant Secretary for Nuclear Energy, 2012.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Additional Creators:
Idaho National Laboratory, United States. Office of the Assistant Secretary for Nuclear Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary:
Quantitative risk assessments are an integral part of risk-informed regulation of current and future nuclear plants in the U.S. The Bayesian approach to uncertainty, in which both stochastic and epistemic uncertainties are represented with precise probability distributions, is the standard approach to modeling uncertainties in such quantitative risk assessments. However, there are long-standing criticisms of the Bayesian approach to epistemic uncertainty from many perspectives, and a number of alternative approaches have been proposed. Among these alternatives, the most promising (and most rapidly developing) would appear to be the concept of imprecise probability. In this paper, we employ a performance indicator example to focus the discussion. We first give a short overview of the traditional Bayesian paradigm and review some its controversial aspects, for example, issues with so-called noninformative prior distributions. We then discuss how the imprecise probability approach treats these issues and compare it with two other approaches: sensitivity analysis and hierarchical Bayes modeling. We conclude with some practical implications for risk-informed decision making.
Report Numbers:
E 1.99:inl/con-11-22093
inl/con-11-22093
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Note:
Published through SciTech Connect.
06/01/2012.
"inl/con-11-22093"
PSAM-11,Helsinki, Finland,06/25/2012,06/29/2012.
Dana Kelly; Robert Youngblood.
Funding Information:
DE-AC07-05ID14517
View MARC record | catkey: 14342670