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.
Publié le : 2010-02-15
Classification:
Mixture Kalman filter,
parameter learning,
particle learning,
sequential inference,
smoothing,
state filtering,
state space models
@article{1280841735,
author = {Carvalho, Carlos M. and Johannes, Michael S. and Lopes, Hedibert F. and Polson, Nicholas G.},
title = {Particle Learning and Smoothing},
journal = {Statist. Sci.},
volume = {25},
number = {1},
year = {2010},
pages = { 88-106},
language = {en},
url = {http://dml.mathdoc.fr/item/1280841735}
}
Carvalho, Carlos M.; Johannes, Michael S.; Lopes, Hedibert F.; Polson, Nicholas G. Particle Learning and Smoothing. Statist. Sci., Tome 25 (2010) no. 1, pp. 88-106. http://gdmltest.u-ga.fr/item/1280841735/