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. © 2010 Institute of Mathematical Statistics.
CITATION STYLE
Carvalho, C. M., Johannes, M. S., Lopes, H. F., & Polson, N. G. (2010). Particle learning and smoothing. Statistical Science, 25(1), 88–106. https://doi.org/10.1214/10-STS325
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