IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v91y2006i10p1435-1442.html
   My bibliography  Save this article

Sensitivity analysis using probability bounding

Author

Listed:
  • Ferson, Scott
  • Troy Tucker, W.

Abstract

Probability bounds analysis (PBA) provides analysts a convenient means to characterize the neighborhood of possible results that would be obtained from plausible alternative inputs in probabilistic calculations. We show the relationship between PBA and the methods of interval analysis and probabilistic uncertainty analysis from which it is jointly derived, and indicate how the method can be used to assess the quality of probabilistic models such as those developed in Monte Carlo simulations for risk analyses. We also illustrate how a sensitivity analysis can be conducted within a PBA by pinching inputs to precise distributions or real values.

Suggested Citation

  • Ferson, Scott & Troy Tucker, W., 2006. "Sensitivity analysis using probability bounding," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1435-1442.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1435-1442
    DOI: 10.1016/j.ress.2005.11.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832005002528
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2005.11.052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jérémy Rohmer & Gonéri Cozannet & Jean-Charles Manceau, 2019. "Addressing ambiguity in probabilistic assessments of future coastal flooding using possibility distributions," Climatic Change, Springer, vol. 155(1), pages 95-109, July.
    2. Ning-Cong Xiao & Yan-Feng Li & Le Yu & Zhonglai Wang & Hong-Zhong Huang, 2014. "Saddlepoint approximation-based reliability analysis method for structural systems with parameter uncertainties," Journal of Risk and Reliability, , vol. 228(5), pages 529-540, October.
    3. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    4. Eldred, M.S. & Swiler, L.P. & Tang, G., 2011. "Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1092-1113.
    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. Hongjie Tang & Shicheng Zhang & Jinhui Li & Lingwei Kong & Baoqiang Zhang & Fei Xing & Huageng Luo, 2023. "Imprecise P-Box Sensitivity Analysis of an Aero-Engine Combustor Performance Simulation Model Considering Correlated Variables," Energies, MDPI, vol. 16(5), pages 1-22, March.
    7. Flage, Roger & Aven, Terje & Berner, Christine L., 2018. "A comparison between a probability bounds analysis and a subjective probability approach to express epistemic uncertainties in a risk assessment context – A simple illustrative example," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 1-10.
    8. Rocchetta, Roberto & Patelli, Edoardo, 2020. "A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    10. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    11. Ning-Cong Xiao & Hong-Zhong Huang & Yan-Feng Li & Zhonglai Wang & Xiao-Ling Zhang, 2013. "Non-probabilistic reliability sensitivity analysis of the model of structural systems with interval variables whose state of dependence is determined by constraints," Journal of Risk and Reliability, , vol. 227(5), pages 491-498, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1435-1442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.