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Integrated treatment of model and parameter uncertainties through a Bayesian approach

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  • Enrique López Droguett
  • Ali Mosleh

Abstract

Bayesian and non-Bayesian approaches have been proposed for treating model uncertainty; in general, model and parameter uncertainties have been tackled as separate domains. This article discusses a Bayesian framework for an integrated assessment of model and parameter uncertainties. The approach accommodates cases involving multiple dependent models, and we also demonstrate that under certain conditions, the model uncertainty assessment approaches known as model averaging and uncertainty-factor are special cases of the proposed formulation. These features are also demonstrated by means of a few examples of interest in the risk and safety domain.

Suggested Citation

  • Enrique López Droguett & Ali Mosleh, 2013. "Integrated treatment of model and parameter uncertainties through a Bayesian approach," Journal of Risk and Reliability, , vol. 227(1), pages 41-54, February.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:1:p:41-54
    DOI: 10.1177/1748006X12461332
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    References listed on IDEAS

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    1. Mohamed N. Jouini & Robert T. Clemen, 1996. "Copula Models for Aggregating Expert Opinions," Operations Research, INFORMS, vol. 44(3), pages 444-457, June.
    2. A. John Bailer & Robert B. Noble & Matthew W. Wheeler, 2005. "Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 291-299, April.
    3. Enrique López Droguett & Ali Mosleh, 2008. "Bayesian Methodology for Model Uncertainty Using Model Performance Data," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1457-1476, October.
    4. Brock, William A. & Durlauf, Steven N. & West, Kenneth D., 2007. "Model uncertainty and policy evaluation: Some theory and empirics," Journal of Econometrics, Elsevier, vol. 136(2), pages 629-664, February.
    5. Jiang, Xiaomo & Mahadevan, Sankaran, 2007. "Bayesian risk-based decision method for model validation under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 707-718.
    6. Piero Baraldi & Enrico Zio, 2010. "A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories," Risk Analysis, John Wiley & Sons, vol. 30(7), pages 1139-1156, July.
    7. Robert T. Clemen & Robert L. Winkler, 1993. "Aggregating Point Estimates: A Flexible Modeling Approach," Management Science, INFORMS, vol. 39(4), pages 501-515, April.
    8. Hojin Moon & Hyun‐Joo Kim & James J. Chen & Ralph L. Kodell, 2005. "Model Averaging Using the Kullback Information Criterion in Estimating Effective Doses for Microbial Infection and Illness," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1147-1159, October.
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    Cited by:

    1. das Chagas Moura, Márcio & Azevedo, Rafael Valença & Droguett, Enrique López & Chaves, Leandro Rego & Lins, Isis Didier & Vilela, Romulo Fernando & Filho, Romero Sales, 2016. "Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 136-146.

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