Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filters constitute the most popular sequential Monte Carlo (SMC) methods. This is a relatively recent development, and the aim here is to provide a brief exposition of these SMC methods and how they are key enabling algorithms in solving nonlinear system identification problems. The particle filters are important for both frequentist (maximum likelihood) and Bayesian nonlinear system identification.
KeywordsParticle filter Particle smoother Sequential Monte Carlo Maximum likelihood Bayesian MCMC Particle MCMC and Backward simulation
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