Abstract:Problems like improper sampling (sampling on unnecessary variables) and undefined prior distribution (or taking random priors) often occur in model updating. Any such limitations on model parameters can lead to lower accuracy and higher experimental costs (due to more iterations) of structural optimisation. In this work, we explored the effective dimensionality of the model updating problem by leveraging the causal information. In order to utilise the causal structure between the parameters, we used Causal Bay… Show more
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