  This is a short review of Monte Carlo methods for approximating filter
distributions in state space models. The basic algorithm and different
strategies to reduce imbalance of the weights are discussed. Finally, methods
for more difficult problems like smoothing and parameter estimation and
applications outside the state space model context are presented.
