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 |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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