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

C.L. Alston and K.L. Mengersen

Computational Statistics & Data Analysis, 2010, vol. 54, issue 4, 916-923

Abstract: 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.

Date: 2010
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