Sequential Monte Carlo Algorithm

AKA: SMC, Particle Filter.
Context:
- It is the Sequential Estimation Algorithm analogue of the Monte Carlo Markov Chain Algorithm.
- …
Counter-Example(s):
- Single-Batch Monte Carlo Algorithm.
See: Recursive Bayesian Estimation, Simulation, Stochastic Approximate Bayesian Inference Algorithm.

References

http://en.wikipedia.org/wiki/Particle_filter
- Particle filters, also known as sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation.
- 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.
(Doucet & de Freitas, 2009) ⇒ Arnaud Doucet, and Nando de Freitas. (2009). “Sequential Monte-Carlo Methods.” Tutorial at NIPS 2009.