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Medientyp:
E-Artikel
Titel:
Particle Learning and Smoothing
Beteiligte:
Carvalho, Carlos M.;
Johannes, Michael S.;
Lopes, Hedibert F.;
Polson, Nicholas G.
Erschienen:
Institute of Mathematical Statistics, 2010
Erschienen in:Statistical Science
Sprache:
Englisch
ISSN:
0883-4237
Entstehung:
Anmerkungen:
Beschreibung:
<p>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.</p>