In this paper we consider parameter estimation of general stochastic nonlinear statespace models using the Maximum Likelihood method. This is accomplished via the employment of an Expectation Maximisation algorithm, where the essential components involve a particle smoother for the expectation step, and a gradient-based search for the maximisation step. The utility of this method is illustrated with several nonlinear and non-Gaussian examples.
17th World Congress of the International Federation of Automatic Control. Proceedings of the 17th World Congress of the International Federation of Automatic Control (Seoul, Korea 6-11 July, 2008) p. 4012-4017