Particle Learning and Smoothing
Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, and Nicholas G. Polson
Source: Statist. Sci. Volume 25, Number 1
(2010), 88-106.
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.
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Keywords: Mixture Kalman filter; parameter learning; particle learning; sequential inference; smoothing; state filtering; state space models
Full-text: Open access
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Permanent link to this document: http://projecteuclid.org/euclid.ss/1280841735
Digital Object Identifier: doi:10.1214/10-STS325
Mathematical Reviews number (MathSciNet): MR2741816
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