We show that efficient importance sampling for nonlinear non-Gaussian state space models can be implemented by computationally efficient Kalman filter and smoothing methods. The result provides some new insights but it primarily leads to a simple and fast method for efficient importance...

This version has replaced the version of January 30, 2012.<P> A successful construction of an importance density for nonlinear non-Gaussian state space models is crucial when Monte Carlo simulation methods are used for likelihood evaluation, signal extraction of dynamic latent factors and...</p>