A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.


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    Titel :

    Modeling Dependence and Assessing the Effect of Uncertainty in Dependence in Probabilistic Analysis and Decision Under Uncertainty


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE 2010 World Congress & Exhibition ; 2010



    Erscheinungsdatum :

    12.04.2010




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





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