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On selecting a prior for the precision parameter of Dirichlet process mixture models

Journal of Statistical Planning and Inference

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DOI: 10.1016/j.jspi.2009.03.009

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Abstract

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G0. In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
On selecting a prior for the precision parameter of Dirichlet process mixture models
Series title:
Journal of Statistical Planning and Inference
DOI:
10.1016/j.jspi.2009.03.009
Volume
139
Issue:
9
Year Published:
2009
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of Statistical Planning and Inference
First page:
3384
Last page:
3390