Bayesian System Identification of MDOF Nonlinear Systems using Highly Informative Training Data

The aim of this paper is to utilise the concept of ‘highly informative training data’ such that, using Markov chain Monte Carlo (MCMC) methods, one can apply Bayesian system identification to multi-degree-offreedom nonlinear systems with relatively little computational cost. Specifically, the Shannon entropy is used as a measure of information content such that, by analysing the information content of the posterior parameter distribution, one is able to select and utilise a relatively small but highly informative set of training data (thus reducing the cost of running MCMC).