  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
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.
