Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution
Mechanical Systems and Signal Processing(2018)
摘要
•Particle filter struggles in state-space models with highly informative observations.•For learning parameters in such models, a novel tempering scheme is proposed.•We use an SMC sampler with the proposed tempering scheme to learn parameters.•Performs well on the Wiener-Hammerstein benchmark.
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关键词
Probabilistic modelling,Bayesian methods,Nonlinear system identification,Sequential Monte Carlo,Particle filter,Approximate Bayesian computations,Highly informative observations,Tempering,Wiener-Hammerstein model
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