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Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler

Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler
Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler
In this paper many convergence issues concerning the implementation of the Gibbs sampler are investigated. Exact computable rates of convergence for Gaussian target distributions are obtained. Different random and non-random updating strategies and blocking combinations are compared using the rates. The effect of dimensionality and correlation structure on the convergence rates are studied. Some examples are considered to demonstrate the results. For a Gaussian image analysis problem several updating strategies are described and compared. For problems in Bayesian linear models several possible parameterizations are analysed in terms of their convergence rates characterizing the optimal choice.
0035-9246
291-317
Roberts, G.O.
c954867f-84a1-4fc7-a618-ccbce35fa89b
Sahu, S.K.
33f1386d-6d73-4b60-a796-d626721f72bf
Roberts, G.O.
c954867f-84a1-4fc7-a618-ccbce35fa89b
Sahu, S.K.
33f1386d-6d73-4b60-a796-d626721f72bf

Roberts, G.O. and Sahu, S.K. (1997) Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler. Journal of the Royal Statistical Society. Series B (Methodological), 59 (2), 291-317. (doi:10.1111/1467-9868.00070).

Record type: Article

Abstract

In this paper many convergence issues concerning the implementation of the Gibbs sampler are investigated. Exact computable rates of convergence for Gaussian target distributions are obtained. Different random and non-random updating strategies and blocking combinations are compared using the rates. The effect of dimensionality and correlation structure on the convergence rates are studied. Some examples are considered to demonstrate the results. For a Gaussian image analysis problem several updating strategies are described and compared. For problems in Bayesian linear models several possible parameterizations are analysed in terms of their convergence rates characterizing the optimal choice.

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More information

Published date: 1997
Organisations: Statistics

Identifiers

Local EPrints ID: 30018
URI: http://eprints.soton.ac.uk/id/eprint/30018
ISSN: 0035-9246
PURE UUID: 67906d9f-01e0-4c06-bf56-301cb5e74863
ORCID for S.K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

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Date deposited: 11 May 2007
Last modified: 16 Mar 2024 03:15

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Contributors

Author: G.O. Roberts
Author: S.K. Sahu ORCID iD

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