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
TypeArticle accepté pour publication ou publié
Journal nameStatistical Methodology
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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 / KeywordsMonte Carlo methods; Bayes estimation; Partition decomposition; Posterior probabilities; Gibbs sampling
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Wraith, Darren; Cappé, Olivier; Cardoso, Jean-François; Fort, Gersende; Prunet, Simon; Kilbinger, Martin; Benabed, Karim; Robert, Christian P. (2009-03) Article accepté pour publication ou publié