Posterior Simulation of Normalized Random Measure Mixtures

Griffin, Jim E. and 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. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)

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
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 04 Jan 2010 12:19
Last Modified: 21 May 2014 11:24
Resource URI: http://kar.kent.ac.uk/id/eprint/23493 (The current URI for this page, for reference purposes)
  • Depositors only (login required):