Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9928
Title: On Bayesian nonparametric modelling of two correlated distributions
Authors: Kolossiatis, Michalis 
Griffin, Jim E. 
Steel, Mark F J 
Keywords: Dependent Dirichlet process;Markov chain Monte Carlo;Normalised random measures;Pólya-urn scheme;Split-merge move
Category: Mathematics
Field: Natural Sciences
Issue Date: 1-Jan-2013
Publisher: Springer Verlag
Source: Statistics and Computing Volume 23, Issue 1, 2013, Pages 1-15
metadata.dc.doi: 10.1007/s11222-011-9283-7
Abstract: In this paper, we consider the problem of modelling a pair of related distributions using Bayesian nonparametric methods. A representation of the distributions as weighted sums of distributions is derived through normalisation. This allows us to define several classes of nonparametric priors. The properties of these distributions are explored and efficient Markov chain Monte Carlo methods are developed. The methodology is illustrated on simulated data and an example concerning hospital efficiency measurement.
URI: http://ktisis.cut.ac.cy/handle/10488/9928
ISSN: 09603174
Rights: © 2011 Springer Science+Business Media, LLC.
Type: Article
Appears in Collections:Άρθρα/Articles

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