Particle Filter
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High Quality Content by WIKIPEDIA articles! Particle filters, also known as sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation. Particle filters have important applications in econometrics. They are usually used to estimate Bayesian models and are the sequential ('on-line') analogue of Markov chain Monte Carlo (MCMC) batch methods and are often similar to importance sampling methods. Well-designed particle filters can often be much faster than MCMC. They are often an alternative to the Extended Kalman filter (EKF) or Unscented Kalman filter…mehr

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High Quality Content by WIKIPEDIA articles! Particle filters, also known as sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation. Particle filters have important applications in econometrics. They are usually used to estimate Bayesian models and are the sequential ('on-line') analogue of Markov chain Monte Carlo (MCMC) batch methods and are often similar to importance sampling methods. Well-designed particle filters can often be much faster than MCMC. They are often an alternative to the Extended Kalman filter (EKF) or Unscented Kalman filter (UKF) with the advantage that, with sufficient samples, they approach the Bayesian optimal estimate, so they can be made more accurate than either the EKF or UKF. The approaches can also be combined by using a version of the Kalman filter as a proposal distribution for the particle filter.