Chapter

Particle Learning for Sequential Bayesian Computation*

Hedibert F. Lopes, Michael S. Johannes, Carlos M. Carvalho and Nicholas G. Polson

in Bayesian Statistics 9

Published in print October 2011 | ISBN: 9780199694587
Published online January 2012 | e-ISBN: 9780191731921 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199694587.003.0011
Particle Learning for Sequential Bayesian Computation*

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Particle learning provides a simulation‐based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive distribution and propagation rule to build a resampling‐sampling framework. Predictive inference and sequential Bayes factors are a direct by‐product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.

Keywords: Particle Learning; Bayesian; Dynamic Factor Models; Essential state vector; Mixture models; Sequential inference; conditional dynamic linear models; nonparametric; Dirichlet

Chapter.  24152 words.  Illustrated.

Subjects: Probability and Statistics

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