Particle filter

Particle filters, also known as Sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation.

Related Topics:
Monte Carlo method - Estimation - Simulation

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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. They are often an alternative to the Extended Kalman filter (EKF) with the advantage that, with sufficient samples, they approach the Bayesian optimal estimate, so they can be made more accurate than the EKF. If well designed can be much faster than MCMC .

Related Topics:
Bayesian - Markov Chain Monte Carlo - Importance sampling - Kalman filter

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