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Data: 15/06/2012
Horário: 14:30
Local: Dep. de Estatística - sala 34
Palestrante: Hedibert Freitas Lopes
Associate Professor of Econometrics and Statistics
The University of Chicago Booth School of Business
Título: Particle filters: state and parameter learning
Resumo:
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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