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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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    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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