Open Access
September 2013 Particle filters
Hans R. Künsch
Bernoulli 19(4): 1391-1403 (September 2013). DOI: 10.3150/12-BEJSP07


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.


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Hans R. Künsch. "Particle filters." Bernoulli 19 (4) 1391 - 1403, September 2013.


Published: September 2013
First available in Project Euclid: 27 August 2013

zbMATH: 1275.93058
MathSciNet: MR3102556
Digital Object Identifier: 10.3150/12-BEJSP07

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

Rights: Copyright © 2013 Bernoulli Society for Mathematical Statistics and Probability

Vol.19 • No. 4 • September 2013
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