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Bayesian optimization: A framework for optimal computational effort for experimental design

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

DOE on models involving time or space dynamics is often very computationally demanding. Predicting a single experimental outcome may require significant computation, let alone evaluating a design criterion and optimizing it with respect to design parameters. To find the exact optimum of the design criterion would typically take infinite computation, and any finite computation will yield a result possessing some uncertainty (due to approximation of the design criterion as well as stopping the optimization procedure). Ideally, one would like to optimize not only the design criterion, but also the way it is approximated and optimized in order to get the largest likely improvement in the design criterion relative to the computational effort spent. Using a Bayesian method for the optimization of the design criterion (not only for calculating the design criterion) can accomplish such an optimal trade-off between (computational) resources spent planning the experiment and expected gain from carrying it out. This talk will lay out the concepts and theory necessary to perform a fully Bayesian optimization that maximizes the expected improvement of the design criterion in relation the computational effort spent.

This talk is part of the Isaac Newton Institute Seminar Series series.

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