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arXiv · 1011.1098

Particle Learning and Smoothing

Carlos M. Carvalho·Michael S. Johannes·Hedibert F. Lopes·Nicholas G. Polson
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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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Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, Nicholas G. Polson. 2010-11-04. Particle Learning and Smoothing. https://doi.org/10.1214/10-sts325

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