  The use of the Bayesian tools in system identification and model updating
paradigms has been increased in the last ten years. Usually, the Bayesian
techniques can be implemented to incorporate the uncertainties associated with
measurements as well as the prediction made by the finite element model (FEM)
into the FEM updating procedure. In this case, the posterior distribution
function describes the uncertainty in the FE model prediction and the
experimental data. Due to the complexity of the modeled systems, the analytical
solution for the posterior distribution function may not exist. This leads to
the use of numerical methods, such as Markov Chain Monte Carlo techniques, to
obtain approximate solutions for the posterior distribution function. In this
paper, a Differential Evaluation Markov Chain Monte Carlo (DE-MC) method is
used to approximate the posterior function and update FEMs. The main idea of
the DE-MC approach is to combine the Differential Evolution, which is an
effective global optimization algorithm over real parameter space, with Markov
Chain Monte Carlo (MCMC) techniques to generate samples from the posterior
distribution function. In this paper, the DE-MC method is discussed in detail
while the performance and the accuracy of this algorithm are investigated by
updating two structural examples.
