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Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty

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  • Helton, J.C.
  • Johnson, J.D.
  • Oberkampf, W.L.
  • Sallaberry, C.J.

Abstract

Three applications of sampling-based sensitivity analysis in conjunction with evidence theory representations for epistemic uncertainty in model inputs are described: (i) an initial exploratory analysis to assess model behavior and provide insights for additional analysis; (ii) a stepwise analysis showing the incremental effects of uncertain variables on complementary cumulative belief functions and complementary cumulative plausibility functions; and (iii) a summary analysis showing a spectrum of variance-based sensitivity analysis results that derive from probability spaces that are consistent with the evidence space under consideration.

Suggested Citation

  • Helton, J.C. & Johnson, J.D. & Oberkampf, W.L. & Sallaberry, C.J., 2006. "Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1414-1434.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1414-1434
    DOI: 10.1016/j.ress.2005.11.055
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    1. Kangas, Annika S. & Kangas, Jyrki, 2004. "Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis," Forest Policy and Economics, Elsevier, vol. 6(2), pages 169-188, March.
    2. Helton, J.C. & Johnson, J.D. & Oberkampf, W.L., 2006. "Probability of loss of assured safety in temperature dependent systems with multiple weak and strong links," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 320-348.
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    1. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. González, Esteban Le Maitre & Desforges, Xavier & Archimède, Bernard, 2018. "Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 403-415.
    3. Helton, Jon C. & Johnson, Jay D., 2011. "Quantification of margins and uncertainties: Alternative representations of epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1034-1052.
    4. Curto, D. & Acebes, F. & González-Varona, J.M. & Poza, D., 2022. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," International Journal of Production Economics, Elsevier, vol. 253(C).
    5. Schöbi, Roland & Sudret, Bruno, 2019. "Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 129-141.
    6. Pi, Shiqiang & Xiao, Longyuan, 2020. "Investigation of temperature-dependent high consequence system with weak and strong links based on probability of loss of assured safety," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. David Curto & Fernando Acebes & Jose M Gonzalez-Varona & David Poza, 2024. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," Papers 2406.03500, arXiv.org.
    8. McFarland, John & DeCarlo, Erin, 2020. "A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Limbourg, Philipp & de Rocquigny, Etienne, 2010. "Uncertainty analysis using evidence theory – confronting level-1 and level-2 approaches with data availability and computational constraints," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 550-564.
    10. Pi, Shiqiang & Liu, Ying & Chen, Haiyan & Deng, Yan & Xiao, Longyuan, 2021. "Probability of loss of assured safety in systems with weak and strong links subject to dependent failures and random shocks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    11. Simon, C. & Weber, P. & Evsukoff, A., 2008. "Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 950-963.
    12. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    13. Helton, J.C. & Johnson, J.D. & Oberkampf, W.L., 2007. "Verification of the calculation of probability of loss of assured safety in temperature-dependent systems with multiple weak and strong links," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1363-1373.
    14. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.
    15. Zhang, Z. & Jiang, C. & Wang, G.G. & Han, X., 2015. "First and second order approximate reliability analysis methods using evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 40-49.

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