Journal of Simulation

, Volume 1, Issue 2, pp 97–107

Bayesian methodology for dynamic modelling


DOI: 10.1057/palgrave.jos.4250014

Cite this article as:
Currie, C. J Simulation (2007) 1: 97. doi:10.1057/palgrave.jos.4250014


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.


Bayesian statistics disease modelling simulation 

Copyright information

© Palgrave Macmillan Ltd 2007

Authors and Affiliations

  1. 1.School of Mathematics, University of SouthamptonSouthamptonUK

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