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Bayesian methodology for dynamic modelling

Record type: Article

We describe a Bayesian methodology for fitting deterministic dynamic models, demonstrating how this can be used to estimate the uncertainty around model outputs. By its nature, Bayesian statistics allows all available sources of information to be incorporated: prior knowledge of the model parameter values and data corresponding to the model outputs, thus allowing for a thorough analysis of the uncertainty. The methodology is demonstrated with an example: a deterministic compartmental model of tuberculosis and HIV disease. We discuss how this method might be modified to allow a similar analysis of stochastic simulation models.

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Citation

Currie, C.S.M. (2007) Bayesian methodology for dynamic modelling Journal of Simulation, 1, (2), pp. 97-107. (doi:10.1057/palgrave.jos.4250014).

More information

Published date: May 2007
Keywords: Bayesian statistics, disease modelling, simulation
Organisations: Operational Research

Identifiers

Local EPrints ID: 54094
URI: http://eprints.soton.ac.uk/id/eprint/54094
ISSN: 1747-7778
PURE UUID: 95c1743a-cc77-4e7a-9dcf-0d7a29188243

Catalogue record

Date deposited: 28 Jul 2008
Last modified: 17 Jul 2017 14:36

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Contributors

Author: C.S.M. Currie

University divisions


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