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Two levels of Bayesian model averaging for optimal control of stochastic systems

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  • Paul Darwen

Abstract

Bayesian model averaging provides the best possible estimate of a model, given the data. This article uses that approach twice: once to get a distribution of plausible models of the world, and again to find a distribution of plausible control functions. The resulting ensemble gives control instructions different from simply taking the single best-fitting model and using it to find a single lowest-error control function for that single model. The only drawback is, of course, the need for more computer time: this article demonstrates that the required computer time is feasible. The test problem here is from flood control and risk management.

Suggested Citation

  • Paul Darwen, 2013. "Two levels of Bayesian model averaging for optimal control of stochastic systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(2), pages 201-213.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:2:p:201-213
    DOI: 10.1080/00207721.2011.598963
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    Cited by:

    1. Sajid Ali & Muhammad Aslam & Mohsin Ali, 2014. "Heterogeneous data analysis using a mixture of Laplace models with conjugate priors," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2619-2636, December.

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