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Topics in Modal Analysis II, Volume 8
The purpose of this contribution is to illustrate the potential of Reversible Jump Markov Chain Monte Carlo (RJMCMC) methods for nonlinear system identification. Markov Chain Monte Carlo (MCMC) sampling methods have come to be viewed as a standard tool for tackling the issue of parameter estimation using Bayesian inference. A limitation of standard MCMC approaches is that they are not suited to tackling the issue of model selection. RJMCMC offers a powerful extension to standard MCMC approachesdoi:10.1007/978-3-319-04774-4_27 fatcat:k4tlrdtu4rfsrnwcpjhtr6x42i