We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...

The use of the Kalman filter for estimation purposes is not always an easy task despite the obvious advantages in many situations of the state-space representation. This is in part due to the fact that the computation of the corresponding score (gradient of the log-likelihood) is sometimes...

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...