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An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties

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  • Krzykacz-Hausmann, Bernard

Abstract

This paper focuses on sensitivity analysis of results from computer models in which both epistemic and aleatory uncertainties are present. Sensitivity is defined in the sense of “uncertainty importance†in order to identify and to rank the principal sources of epistemic uncertainty. A natural and consistent way to arrive at sensitivity results in such cases would be a two-dimensional or double-loop nested Monte Carlo sampling strategy in which the epistemic parameters are sampled in the outer loop and the aleatory variables are sampled in the nested inner loop. However, the computational effort of this procedure may be prohibitive for complex and time-demanding codes. This paper therefore suggests an approximate method for sensitivity analysis based on particular one-dimensional or single-loop sampling procedures, which require substantially less computational effort. From the results of such sampling one can obtain approximate estimates of several standard uncertainty importance measures for the aleatory probability distributions and related probabilistic quantities of the model outcomes of interest. The reliability of the approximate sensitivity results depends on the effect of all epistemic uncertainties on the total joint epistemic and aleatory uncertainty of the outcome. The magnitude of this effect can be expressed quantitatively and estimated from the same single-loop samples. The higher it is the more accurate the approximate sensitivity results will be. A case study, which shows that the results from the proposed approximate method are comparable to those obtained with the full two-dimensional approach, is provided.

Suggested Citation

  • Krzykacz-Hausmann, Bernard, 2006. "An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1210-1218.
  • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1210-1218
    DOI: 10.1016/j.ress.2005.11.019
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    Cited by:

    1. Wang, Zhenqiang & Jia, Gaofeng, 2020. "Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. Baustert, Paul & Othoniel, Benoit & Rugani, Benedetto & Leopold, Ulrich, 2018. "Uncertainty analysis in integrated environmental models for ecosystem service assessments: Frameworks, challenges and gaps," Ecosystem Services, Elsevier, vol. 33(PB), pages 110-123.
    3. 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.
    4. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    5. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    6. Jingwen Song & Zhenzhou Lu & Pengfei Wei & Yanping Wang, 2015. "Global sensitivity analysis for model with random inputs characterized by probability-box," Journal of Risk and Reliability, , vol. 229(3), pages 237-253, June.
    7. Zio, E. & Pedroni, N., 2009. "Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1764-1781.
    8. Luyi Li & Zhenzhou Lu, 2016. "A new algorithm for importance analysis of the inputs with distribution parameter uncertainty," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3065-3077, October.

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