Bernoulli

  • Bernoulli
  • Volume 19, Number 4 (2013), 1391-1403.

Particle filters

Hans R. Künsch

Full-text: Open access

Abstract

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.

Article information

Source
Bernoulli, Volume 19, Number 4 (2013), 1391-1403.

Dates
First available in Project Euclid: 27 August 2013

Permanent link to this document
https://projecteuclid.org/euclid.bj/1377612857

Digital Object Identifier
doi:10.3150/12-BEJSP07

Mathematical Reviews number (MathSciNet)
MR3102556

Zentralblatt MATH identifier
1275.93058

Keywords
Ensemble Kalman filter importance sampling and resampling sequential Monte Carlo smoothing algorithm state space models

Citation

Künsch, Hans R. Particle filters. Bernoulli 19 (2013), no. 4, 1391--1403. doi:10.3150/12-BEJSP07. https://projecteuclid.org/euclid.bj/1377612857


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