Open Access
February 2010 Particle Learning and Smoothing
Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, Nicholas G. Polson
Statist. Sci. 25(1): 88-106 (February 2010). DOI: 10.1214/10-STS325


Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.


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Carlos M. Carvalho. Michael S. Johannes. Hedibert F. Lopes. Nicholas G. Polson. "Particle Learning and Smoothing." Statist. Sci. 25 (1) 88 - 106, February 2010.


Published: February 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1328.62541
MathSciNet: MR2741816
Digital Object Identifier: 10.1214/10-STS325

Keywords: Mixture Kalman filter , parameter learning , particle learning , sequential inference , smoothing , state filtering , state space models

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.25 • No. 1 • February 2010
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