A Bayesian framework for uncertainty formulation of engineering design proces

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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    Abstract

    Uncertainties in the design process are investigated in this paper. A formal Bayesian method is presented for designers to quantify uncertainties in design process. The uncertainties are implemented in a decision support system that plays a key role in design of complex projects where a large and multidisciplinary team of engineers are involved. The proposed method produces the probability distribution function of the model score or uncertainty. Two example applications for design and empirical demonstrate application of the method.
    Original languageUndefined
    Title of host publicationUncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012
    EditorsCengiz Kahraman, Etienne Kerre, Faik Tunc Bozbura
    PublisherWorld Scientific
    Pages418-423
    ISBN (Print)9789814417730
    DOIs
    Publication statusPublished - 2012

    Publication series

    NameWorld Scientific Proceedings Series on Computer Engineering and Information Science
    PublisherWorld Scientific Publishing
    Volume7

    Keywords

    • IR-89051

    Cite this

    Rajabali Nejad, M., & Spitas, C. (2012). A Bayesian framework for uncertainty formulation of engineering design proces. In C. Kahraman, E. Kerre, & F. T. Bozbura (Eds.), Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012 (pp. 418-423). (World Scientific Proceedings Series on Computer Engineering and Information Science; Vol. 7). World Scientific. https://doi.org/10.1142/9789814417747_0067
    Rajabali Nejad, Mohammadreza ; Spitas, C. / A Bayesian framework for uncertainty formulation of engineering design proces. Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012. editor / Cengiz Kahraman ; Etienne Kerre ; Faik Tunc Bozbura. World Scientific, 2012. pp. 418-423 (World Scientific Proceedings Series on Computer Engineering and Information Science).
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    abstract = "Uncertainties in the design process are investigated in this paper. A formal Bayesian method is presented for designers to quantify uncertainties in design process. The uncertainties are implemented in a decision support system that plays a key role in design of complex projects where a large and multidisciplinary team of engineers are involved. The proposed method produces the probability distribution function of the model score or uncertainty. Two example applications for design and empirical demonstrate application of the method.",
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    Rajabali Nejad, M & Spitas, C 2012, A Bayesian framework for uncertainty formulation of engineering design proces. in C Kahraman, E Kerre & FT Bozbura (eds), Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012. World Scientific Proceedings Series on Computer Engineering and Information Science, vol. 7, World Scientific, pp. 418-423. https://doi.org/10.1142/9789814417747_0067

    A Bayesian framework for uncertainty formulation of engineering design proces. / Rajabali Nejad, Mohammadreza; Spitas, C.

    Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012. ed. / Cengiz Kahraman; Etienne Kerre; Faik Tunc Bozbura. World Scientific, 2012. p. 418-423 (World Scientific Proceedings Series on Computer Engineering and Information Science; Vol. 7).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

    TY - CHAP

    T1 - A Bayesian framework for uncertainty formulation of engineering design proces

    AU - Rajabali Nejad, Mohammadreza

    AU - Spitas, C.

    PY - 2012

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    N2 - Uncertainties in the design process are investigated in this paper. A formal Bayesian method is presented for designers to quantify uncertainties in design process. The uncertainties are implemented in a decision support system that plays a key role in design of complex projects where a large and multidisciplinary team of engineers are involved. The proposed method produces the probability distribution function of the model score or uncertainty. Two example applications for design and empirical demonstrate application of the method.

    AB - Uncertainties in the design process are investigated in this paper. A formal Bayesian method is presented for designers to quantify uncertainties in design process. The uncertainties are implemented in a decision support system that plays a key role in design of complex projects where a large and multidisciplinary team of engineers are involved. The proposed method produces the probability distribution function of the model score or uncertainty. Two example applications for design and empirical demonstrate application of the method.

    KW - IR-89051

    U2 - 10.1142/9789814417747_0067

    DO - 10.1142/9789814417747_0067

    M3 - Chapter

    SN - 9789814417730

    T3 - World Scientific Proceedings Series on Computer Engineering and Information Science

    SP - 418

    EP - 423

    BT - Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012

    A2 - Kahraman, Cengiz

    A2 - Kerre, Etienne

    A2 - Bozbura, Faik Tunc

    PB - World Scientific

    ER -

    Rajabali Nejad M, Spitas C. A Bayesian framework for uncertainty formulation of engineering design proces. In Kahraman C, Kerre E, Bozbura FT, editors, Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference, Istanbul, Turkey, 26–29 August 2012. World Scientific. 2012. p. 418-423. (World Scientific Proceedings Series on Computer Engineering and Information Science). https://doi.org/10.1142/9789814417747_0067