  Nonlinear non-Gaussian state-space models are ubiquitous in statistics,
econometrics, information engineering and signal processing. Particle methods,
also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical
approximations to the associated state inference problems. However, in most
applications, the state-space model of interest also depends on unknown static
parameters that need to be estimated from the data. In this context, standard
particle methods fail and it is necessary to rely on more sophisticated
algorithms. The aim of this paper is to present a comprehensive review of
particle methods that have been proposed to perform static parameter estimation
in state-space models. We discuss the advantages and limitations of these
methods and illustrate their performance on simple models.
