Practical Bayesian density estimation using mixtures of normals
- 31 August 1997
- journal article
- research article
- Published by JSTOR in Journal of the American Statistical Association
- Vol. 92 (439) , 894-902
- https://doi.org/10.2307/2965553
Abstract
Mixtures of normals provide a flexible model for estimating densities in a Bayesian framework. There are some difficulties with this model, however. First, standard reference priors yield improper posteriors. Second, the posterior for the number of components in the mixture is not well defined (if the reference prior is used). Third, posterior simulation does not provide a direct estimate of the posterior for the number of components. We present some practical methods for coping with these problems. Finally, we give some results on the consistency of the method when the maximum number of components is allowed to grow with the sample size.Keywords
This publication has 3 references indexed in Scilit:
- Bayes FactorsJournal of the American Statistical Association, 1995
- Bayesian Density Estimation and Inference Using MixturesJournal of the American Statistical Association, 1995
- Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in the GalaxiesJournal of the American Statistical Association, 1990