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Bayesian methodology for dynamic modelling

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  • C S M Currie

Abstract

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.

Suggested Citation

  • C S M Currie, 2007. "Bayesian methodology for dynamic modelling," Journal of Simulation, Taylor & Francis Journals, vol. 1(2), pages 97-107, May.
    • Handle: RePEc:taf:tjsmxx:v:1:y:2007:i:2:p:97-107
    DOI: 10.1057/palgrave.jos.4250014
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    Cited by:

    1. Elisa F. Long & Eike Nohdurft & Stefan Spinler, 2018. "Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic Policies," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 181-198, May.

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