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Mixture models, latent variables and partitioned importance sampling

Casella, George; Robert, Christian P.; Wells, Martin T. (2004), Mixture models, latent variables and partitioned importance sampling, Statistical Methodology, 1, 1-2, p. 1-18. http://dx.doi.org/10.1016/j.stamet.2004.05.001

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Type
Article accepté pour publication ou publié
Date
2004
Journal name
Statistical Methodology
Volume
1
Number
1-2
Publisher
Elsevier
Pages
1-18
Publication identifier
http://dx.doi.org/10.1016/j.stamet.2004.05.001
Metadata
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Author(s)
Casella, George
Robert, Christian P.
Wells, Martin T.
Abstract (EN)
Gibbs sampling has had great success in the analysis of mixture models. In particular, the “latent variable” formulation of the mixture model greatly reduces computational complexity. However, one failing of this approach is the possible existence of almost-absorbing states, called trapping states, as it may require an enormous number of iterations to escape from these states. Here we examine an alternative approach to estimation in mixture models, one based on a Rao–Blackwellization argument applied to a latent-variable-based estimator. From this derivation we construct an alternative Monte Carlo sampling scheme that avoids trapping states.
Subjects / Keywords
Monte Carlo methods; Bayes estimation; Partition decomposition; Posterior probabilities; Gibbs sampling
JEL
C11 - Bayesian Analysis: General
C15 - Statistical Simulation Methods: General

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