Monte Carlo approximations for general state space models

Abstract Nonlinear and non-Gaussian state-space models form a large and flexible model class in time series analysis. Two methods for sequentially generating samples from filter densities and smoother densities by simple rejection algorithms are introduced. We illustrate the behavior of our methods in several nonlinear and non-Gaussian examples and compare them with other well-known methods.