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Posterior Simulation of Normalized Random Measure Mixtures

Griffin, Jim E., Walker, Stephen G. (2011) Posterior Simulation of Normalized Random Measure Mixtures. Journal of Computational and Graphical Statistics, 20 (1). pp. 241-259. ISSN 1061-8600. (doi:10.1198/jcgs.2010.08176) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:23493)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1198/jcgs.2010.08176

Abstract

This article describes posterior simulation methods for mixture models whose mixing distribution has a Normalized Random Measure prior. The methods use slice sampling ideas and introduce no truncation error. The approach can be easily applied to both homogeneous and nonhomogeneous Normalized Random Measures and allows the updating of the parameters of the random measure. The methods are illustrated on data examples using both Dirichlet and Normalized Generalized Gamma process priors. In particular, the methods are shown to be computationally competitive with previously developed samplers for Dirichlet process mixture models. Matlab code to implement these methods is available as supplemental material.

Item Type: Article
DOI/Identification number: 10.1198/jcgs.2010.08176
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: 04 Jan 2010 12:19 UTC
Last Modified: 16 Nov 2021 10:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/23493 (The current URI for this page, for reference purposes)

University of Kent Author Information

Griffin, Jim E..

Creator's ORCID: https://orcid.org/0000-0002-4828-7368
CReDIT Contributor Roles:

Walker, Stephen G..

Creator's ORCID:
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