Uncertainty in probabilistic risk assessment

Uncertainty in probabilistic risk assessment Dealing with uncertainty is an important and difficult aspect of analyses for complex systems. Such systems involve many uncertainties, and assessing probabilities to represent these uncertainties is itself a complex undertaking utilizing a variety of information sources. At a very basic level, uncertainty is uncertainty, and attempting to distinguish between ‘types of uncertainty’ is questionable. At a practical level, on the other hand, a close look at such distinctions suggests that they are driven by important modelling issues related to model structuring, probability assessment, information gathering, and sensitivity analysis. Anything that brings more attention to these issues should improve the state of the art. However, I would prefer to attack the issues directly instead of working indirectly through the notion of ‘types of uncertainty.’ http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliability Engineering and System Safety Elsevier

Uncertainty in probabilistic risk assessment

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Publisher
Elsevier
Copyright
Copyright © 1996 Elsevier Ltd
ISSN
0951-8320
eISSN
1879-0836
D.O.I.
10.1016/S0951-8320(96)00070-1
Publisher site
See Article on Publisher Site

Abstract

Dealing with uncertainty is an important and difficult aspect of analyses for complex systems. Such systems involve many uncertainties, and assessing probabilities to represent these uncertainties is itself a complex undertaking utilizing a variety of information sources. At a very basic level, uncertainty is uncertainty, and attempting to distinguish between ‘types of uncertainty’ is questionable. At a practical level, on the other hand, a close look at such distinctions suggests that they are driven by important modelling issues related to model structuring, probability assessment, information gathering, and sensitivity analysis. Anything that brings more attention to these issues should improve the state of the art. However, I would prefer to attack the issues directly instead of working indirectly through the notion of ‘types of uncertainty.’

Journal

Reliability Engineering and System SafetyElsevier

Published: Nov 1, 1996

References

  • Bayesian Data Analysis
    Gelman, A.; Carlin, J.B.; Stern, H.S.; Rubin, D.B.
  • Expert judgment in risk analysis and management: process, context, and pitfalls
    Otway, H.; von Winterfeldt, D.
  • The Principles and Applications of Decision Analysis

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