Allowing for the effect of data binning in a Bayesian Normal mixture model

The usual Gibbs sampling framework of the Bayesian mixture model is extended to account for binned data. This model involves the addition of a latent variable in the model which represents simulated values from the believed true distribution at each iteration of the algorithm. The technique results...

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Veröffentlicht in:	Computational statistics & data analysis 2010-04, Vol.54 (4), p.916-923
Hauptverfasser:	Alston, C.L., Mengersen, K.L.
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Sprache:	eng
Schlagworte:	Distribution theory Exact sciences and technology General topics Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Statistics
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description	The usual Gibbs sampling framework of the Bayesian mixture model is extended to account for binned data. This model involves the addition of a latent variable in the model which represents simulated values from the believed true distribution at each iteration of the algorithm. The technique results in better model fit and recognition of the more subtle aspects of the density of the data.
doi_str_mv	10.1016/j.csda.2009.10.003
format	Article
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subjects	Distribution theory Exact sciences and technology General topics Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Statistics
title	Allowing for the effect of data binning in a Bayesian Normal mixture model
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