Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9928
Title: On Bayesian nonparametric modelling of two correlated distributions
Authors: Kolossiatis, Michalis 
Griffin, Jim E. 
Steel, Mark F J 
Major Field of Science: Natural Sciences
Field Category: Mathematics
Keywords: Dependent Dirichlet process;Markov chain Monte Carlo;Normalised random measures;Pólya-urn scheme;Split-merge move
Issue Date: 2013
Source: Statistics and Computing, 2013, vol. 23, no. 1, pp. 1-15
Volume: 23
Issue: 1
Start page: 1
End page: 15
Journal: Statistics and Computing 
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: https://hdl.handle.net/20.500.14279/9928
ISSN: 15731375
DOI: 10.1007/s11222-011-9283-7
Rights: © Springer
Type: Article
Affiliation : Cyprus University of Technology 
University of Kent at Canterbury 
University of Warwick 
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