  The Finite Element Method (FEM) is generally unable to accurately predict
natural frequencies and mode shapes of structures (eigenvalues and
eigenvectors). Engineers develop numerical methods and a variety of techniques
to compensate for this misalignment of modal properties, between experimentally
measured data and the computed result from the FEM of structures. In this paper
we compare two indirect methods of updating namely, the Adaptive Metropolis
Hastings and a newly applied algorithm called Monte Carlo Dynamically Weighted
Importance Sampling (MCDWIS). The approximation of a posterior predictive
distribution is based on Bayesian inference of continuous multivariate Gaussian
probability density functions, defining the variability of physical properties
affected by forced vibration. The motivation behind applying MCDWIS is in the
complexity of computing normalizing constants in higher dimensional or
multimodal systems. The MCDWIS accounts for this intractability by analytically
computing importance sampling estimates at each time step of the algorithm. In
addition, a dynamic weighting step with an Adaptive Pruned Enriched Population
Control Scheme (APEPCS) allows for further control over weighted samples and
population size. The performance of the MCDWIS simulation is graphically
illustrated for all algorithm dependent parameters and show unbiased, stable
sample estimates.
