Particle Learning and Smoothing
Abstract
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.
Cite
@article{arxiv.1011.1098,
title = {Particle Learning and Smoothing},
author = {Carlos M. Carvalho and Michael S. Johannes and Hedibert F. Lopes and Nicholas G. Polson},
journal= {arXiv preprint arXiv:1011.1098},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/10-STS325 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)