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 currently available from this repository. You may be able to access a copy if URLs are provided)
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.
|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:||https://kar.kent.ac.uk/id/eprint/23493 (The current URI for this page, for reference purposes)|