Kalman and particle filtering
Jesús Fernández‐Villaverde · Palgrave Macmillan UK eBooks · 2010
The Kalman and particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the particle filter does so by a sequential Monte Carlo method. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.