Particle filters
Copyright policy )Particle filters
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.
Published in at http://dx.doi.org/10.3150/12-BEJSP07 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
Sampled-data control/observation systems, FOS: Computer and information sciences, state space models, Computational methods in stochastic control, Mathematics - Statistics Theory, Statistics Theory (math.ST), sequential Monte Carlo, Statistics - Computation, ensemble Kalman filter, Filtering in stochastic control theory, smoothing algorithm, Ensemble Kalman filter, FOS: Mathematics, importance sampling and resampling, sequential Monte Carlo method, Computation (stat.CO)
Sampled-data control/observation systems, FOS: Computer and information sciences, state space models, Computational methods in stochastic control, Mathematics - Statistics Theory, Statistics Theory (math.ST), sequential Monte Carlo, Statistics - Computation, ensemble Kalman filter, Filtering in stochastic control theory, smoothing algorithm, Ensemble Kalman filter, FOS: Mathematics, importance sampling and resampling, sequential Monte Carlo method, Computation (stat.CO)
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