Bayesian treatment of model uncertainty for partially applicable models

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      <subfield code="a">López Droguett, Enrique</subfield>
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      <subfield code="a">Bayesian treatment of model uncertainty for partially applicable models</subfield>
      <subfield code="c">Enrique López Droguett, Ali Mosleh</subfield>
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      <subfield code="a">This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models¿ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.</subfield>
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      <subfield code="w">MAP20077000345</subfield>
      <subfield code="t">Risk analysis : an international journal</subfield>
      <subfield code="d">McLean, Virginia : Society for Risk Analysis, 1987-2015</subfield>
      <subfield code="x">0272-4332</subfield>
      <subfield code="g">03/02/2014 Volumen 34 Número 2 - febrero 2014 </subfield>
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      <subfield code="y">MÁS INFORMACIÓN</subfield>
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