TY - GEN

T1 - Bayesian evidence for finite element model updating

AU - Mthembu, Linda

AU - Marwala, Tshilidzi

AU - Friswell, Michael I.

AU - Adhikari, Sondipon

PY - 2009

Y1 - 2009

N2 - This paper considers the problem of model selection within the context of finite element model updating. Given that a number of FEM updating models, with different updating parameters, can be designed, this paper proposes using the Bayesian evidence statistic to assess the probability of each updating model. This makes it possible then to evaluate the need for alternative updating parameters in the updating of the initial FE model. The model evidences are compared using the Bayes factor, which is the ratio of evidences. The Jeffrey's scale is used to determine the differences in the models. The Bayesian evidence is calculated by integrating the likelihood of the data given the model and its parameters over the a priori model parameter space using the new nested sampling algorithm. The nested algorithm samples this likelihood distribution by using a hard likelihood-value constraint on the sampling region while providing the posterior samples of the updating model parameters as a by-product. This method is used to calculate the evidence of a number of plausible finite element models.

AB - This paper considers the problem of model selection within the context of finite element model updating. Given that a number of FEM updating models, with different updating parameters, can be designed, this paper proposes using the Bayesian evidence statistic to assess the probability of each updating model. This makes it possible then to evaluate the need for alternative updating parameters in the updating of the initial FE model. The model evidences are compared using the Bayes factor, which is the ratio of evidences. The Jeffrey's scale is used to determine the differences in the models. The Bayesian evidence is calculated by integrating the likelihood of the data given the model and its parameters over the a priori model parameter space using the new nested sampling algorithm. The nested algorithm samples this likelihood distribution by using a hard likelihood-value constraint on the sampling region while providing the posterior samples of the updating model parameters as a by-product. This method is used to calculate the evidence of a number of plausible finite element models.

UR - http://www.scopus.com/inward/record.url?scp=84861559624&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84861559624

SN - 9781605609614

T3 - Conference Proceedings of the Society for Experimental Mechanics Series

BT - IMAC-XXVII

T2 - 27th Conference and Exposition on Structural Dynamics 2009, IMAC XXVII

Y2 - 9 February 2009 through 12 February 2009

ER -