MCMC Control Spreadsheets for Exponential Mixture Estimation

This article presents Bayesian inference for exponential mixtures, including the choice of a noninformative prior based on a location-scale reparameterization of the mixture. Adapted control sheets are proposed for studying the convergence of the associated Gibbs sampler. They exhibit a strong lack...

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Published in:Journal of computational and graphical statistics 1999-06, Vol.8 (2), p.298-317
Main Authors: Gruet, Marie-Anne, Philippe, Anne, Robert, Christian P.
Format: Article
Language:English
Subjects:
Allocation map
Central limit theorem
Convergence control
Datasets
Density estimation
Estimators
Gibbs sampler
Histograms
Identifiability
Legends
Life Sciences
Normality test
P values
Parameterization
Reversible jump
Riemann sums
Spreadsheets
Sub-sampling
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container_title Journal of computational and graphical statistics
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description This article presents Bayesian inference for exponential mixtures, including the choice of a noninformative prior based on a location-scale reparameterization of the mixture. Adapted control sheets are proposed for studying the convergence of the associated Gibbs sampler. They exhibit a strong lack of stability in the allocations of the observations to the different components of the mixture. The setup is extended to the case when the number of components in the mixture is unknown and a reversible jump MCMC technique is implemented. The results are illustrated on simulations and a real dataset.
doi_str_mv 10.1080/10618600.1999.10474815
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ispartof Journal of computational and graphical statistics, 1999-06, Vol.8 (2), p.298-317
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subjects Allocation map
Central limit theorem
Convergence control
Datasets
Density estimation
Estimators
Gibbs sampler
Histograms
Identifiability
Legends
Life Sciences
Normality test
P values
Parameterization
Reversible jump
Riemann sums
Spreadsheets
Sub-sampling
title MCMC Control Spreadsheets for Exponential Mixture Estimation
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