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Bayesian numerical analysis: global optimization and other applications

Abstract:

We present a unifying framework for the global optimization of functions which are expensive to evaluate. The framework is based on a Bayesian interpretation of radial basis function interpolation which incorporates existing methods such as Kriging, Gaussian process regression and neural networks. This viewpoint enables the application of Bayesian decision theory to derive a sequential global optimization algorithm which can be extended to include existing algorithms of this type in the li...

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Institution:
University of Oxford
Department:
Mathematical,Physical & Life Sciences Division - Mathematical Institute
Role:
Author

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Role:
Supervisor
Role:
Supervisor
Publication date:
2011
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
URN:
uuid:ab268fe7-f757-459e-b1fe-a4a9083c1cba
Local pid:
ora:6024

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