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

Darwen, Paul J. (2013) Two levels of Bayesian model averaging for optimal control of stochastic systems. International Journal of Systems Science, 44 (2). pp. 201-213.

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View at Publisher Website: http://dx.doi.org/10.1080/00207721.2011....
 
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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.

Item ID: 22363
Item Type: Article (Refereed Research - C1)
Keywords: risk management; rainfall models; Bayesian model averaging; neural networks; control
Additional Information:

Part of a special issue on computational intelligence optimisation in the presence of uncertainties.

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ISSN: 1464-5319
Date Deposited: 02 Aug 2012 04:11
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling @ 50%
01 MATHEMATICAL SCIENCES > 0104 Statistics > 010406 Stochastic Analysis and Modelling @ 40%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 10%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 50%
96 ENVIRONMENT > 9609 Land and Water Management > 960903 Coastal and Estuarine Water Management @ 30%
96 ENVIRONMENT > 9609 Land and Water Management > 960913 Water Allocation and Quantification @ 20%
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