Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010
19 Pages Posted: 22 Oct 2011 Last revised: 9 Nov 2011
Date Written: 2010
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.
Suggested Citation: Suggested Citation
Carvalho, Carlos M. and Johannes, Michael S. and Lopes, Hedibert F. and Polson, Nick, Particle Learning and Smoothing (2010). Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010 . Available at SSRN: https://ssrn.com/abstract=1947050