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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Citation

Johannes, Michael, Carlos Carvalho, Hedibert Lopes, and Nicholas Polson. "Particle Learning and Smoothing." Statistical Science 25, no. 1 (2010): 88-106.

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