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Calculation of marginal densities for parameters of multinomial distributions

Calculation of marginal densities for parameters of multinomial distributions
Calculation of marginal densities for parameters of multinomial distributions
The full Bayesian analysis of multinomial data using informative and flexible prior distributions has, in the past, been restricted by the technical problems involved in performing the numerical integrations required to obtain marginal densities for parameters and other functions thereof. In this paper it is shown that Gibbs sampling is suitable for obtaining accurate approximations to marginal densities for a large and flexible family of posterior distributions—the family. The method is illustrated with a three-way contingency table. Two alternative Monte Carlo strategies are also discussed.
multinomial distribution, bayesian analysis, gibbs sampling, Å family
0960-3174
279-286
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Skene, Allan M.
f0f9505d-4244-479f-9116-68b879d154d1
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Skene, Allan M.
f0f9505d-4244-479f-9116-68b879d154d1

Forster, Jonathan J. and Skene, Allan M. (1994) Calculation of marginal densities for parameters of multinomial distributions. Statistics and Computing, 4 (4), 279-286. (doi:10.1007/BF00156751).

Record type: Article

Abstract

The full Bayesian analysis of multinomial data using informative and flexible prior distributions has, in the past, been restricted by the technical problems involved in performing the numerical integrations required to obtain marginal densities for parameters and other functions thereof. In this paper it is shown that Gibbs sampling is suitable for obtaining accurate approximations to marginal densities for a large and flexible family of posterior distributions—the family. The method is illustrated with a three-way contingency table. Two alternative Monte Carlo strategies are also discussed.

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More information

Published date: 1994
Keywords: multinomial distribution, bayesian analysis, gibbs sampling, Å family
Organisations: Statistics

Identifiers

Local EPrints ID: 46379
URI: http://eprints.soton.ac.uk/id/eprint/46379
ISSN: 0960-3174
PURE UUID: 6eb45d5e-13cf-460f-aa79-a4eb5bef414e
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 25 Jun 2007
Last modified: 16 Mar 2024 02:45

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Contributors

Author: Jonathan J. Forster ORCID iD
Author: Allan M. Skene

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