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Default priors for density estimation with mixture models

Griffin, Jim E. (2010) Default priors for density estimation with mixture models. Bayesian Analysis, 5 (1). pp. 45-64. ISSN 1931-6690. (doi:10.1214/10-BA502) (KAR id:23865)

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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.

Item Type: Article
DOI/Identification number: 10.1214/10-BA502
Uncontrolled keywords: Density Estimation; Dirichlet process mixture models; Mixtures of normals; Normalized Generalized Gamma processes
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 29 Jun 2011 13:34 UTC
Last Modified: 16 Nov 2021 10:02 UTC
Resource URI: (The current URI for this page, for reference purposes)
Griffin, Jim E.:

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