On Bayesian nonparametric modelling of two correlated distributions

Kolossiatis, Michalis, Griffin, Jim E., Steel, Mark F.J. (2013) On Bayesian nonparametric modelling of two correlated distributions. Statistics and Computing, 23 (1). pp. 1-15. ISSN 0960-3174. (doi:10.1007/s11222-011-9283-7) (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) (KAR id:36079)

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.
Official URL: http://dx.doi.org/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.

Item Type:	Article
DOI/Identification number:	10.1007/s11222-011-9283-7
Uncontrolled keywords:	Dependent Dirichlet process; Markov chain Monte Carlo; Normalised Random Measures; Polya-urn scheme; Split-Merge move
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User:	Michalis Kolossiatis
Date Deposited:	06 Nov 2013 18:48 UTC
Last Modified:	05 Nov 2024 10:19 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/36079 (The current URI for this page, for reference purposes)

University of Kent Author Information

Kolossiatis, Michalis.

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CReDIT Contributor Roles:

Griffin, Jim E..

Creator's ORCID:	https://orcid.org/0000-0002-4828-7368
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