Particle Learning and Smoothing

  • Carvalho C
  • Johannes M
  • Lopes H
 et al. 
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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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Authors

  • Carlos M. Carvalho

  • Michael S. Johannes

  • Hedibert F. Lopes

  • Nicholas G. Polson

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