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On convergence of the EM algorithm and the Gibbs sampler

Record type: Article

In this article we investigate the relationship between the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM-type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be directly transported to the other. In particular, by running the EM algorithm we know approximately how many iterations are needed for convergence of the Gibbs sampler. We also obtain a result that under certain conditions, the EM algorithm used for finding the maximum likelihood estimates can be slower to converge than the corresponding Gibbs sampler for Bayesian inference. We illustrate our results in a number of realistic examples all based on the generalized linear mixed models.

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Citation

Sahu, Sujit K. and Roberts, Gareth O. (1999) On convergence of the EM algorithm and the Gibbs sampler Statistics and Computing, 9, (1), pp. 55-64. (doi:10.1023/A:1008814227332).

More information

Published date: 1999
Keywords: gaussian distribution, generalized linear mixed models, markov chain monte carlo, parameterization, rate of convergence
Organisations: Statistics

Identifiers

Local EPrints ID: 30031
URI: http://eprints.soton.ac.uk/id/eprint/30031
ISSN: 0960-3174
PURE UUID: 372ca272-8c95-440c-b723-8c79ef5b5ee4

Catalogue record

Date deposited: 11 May 2007
Last modified: 17 Jul 2017 15:56

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Contributors

Author: Sujit K. Sahu
Author: Gareth O. Roberts

University divisions


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