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We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare(More)
This paper is concerned with the use of split-Gaussian importance distributions in sequential importance resampling based particle filtering. We present novel particle filtering algorithms using the split-Gaussian importance distributions and compare their performance with several alternatives. Using a univariate nonlinear reference model, we compare the(More)
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