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March 2010 Default priors for density estimation with mixture models
J. E. Griffin
Bayesian Anal. 5(1): 45-64 (March 2010). DOI: 10.1214/10-BA502

Abstract

The infinite mixture of normals model has become a popular method for density estimation problems. This paper proposes an alternative hierarchical model that leads to hyperparameters that can be interpreted as the location, scale and smoothness of the density. The priors on other parts of the model have little effect on the density estimates and can be given default choices. Automatic Bayesian density estimation can be implemented by using uninformative priors for location and scale and default priors for the smoothness. The performance of these methods for density estimation are compared to previously proposed default priors for four data sets.

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J. E. Griffin. "Default priors for density estimation with mixture models." Bayesian Anal. 5 (1) 45 - 64, March 2010. https://doi.org/10.1214/10-BA502

Information

Published: March 2010
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62127
MathSciNet: MR2596435
Digital Object Identifier: 10.1214/10-BA502

Rights: Copyright © 2010 International Society for Bayesian Analysis

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