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Bayesian Optimization with Application to Computer Experiments

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-3-030-82458-7

ISBN: 978-3-030-82457-0

Publisher: Springer International Publishing - Springer Journals

Aug 3, 2021

Available

Computer Experiments

Pourmohamad, Tony; Lee, Herbert K. H.

Pages: 1 –17

[This chapter introduces the book with several examples of computer models and associated optimization problems. It also provides an introduction to space-filling designs, with a focus on Latin hypercube designs.]
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Surrogate Models

Pourmohamad, Tony; Lee, Herbert K. H.

Pages: 19 –32

[This chapter provides background on statistical models used to emulate computer simulators. These models are also referred to as surrogate models. The primary focus is on Gaussian processes, which are used throughout the book. Simple examples demonstrate various aspects of the models. Treed...
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Unconstrained Optimization

Pourmohamad, Tony; Lee, Herbert K. H.

Pages: 33 –68

[This chapter discusses Bayesian Optimization for problems without constraints. A general algorithm is introduced, along with discussion of the importance of the choice of the acquisition function. Among a larger list of examples of acquisition functions, three such functions are examined in more...
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Constrained Optimization

Pourmohamad, Tony; Lee, Herbert K. H.

Pages: 69 –94

[This chapter discusses the case of constrained optimization, and the additional steps needed to perform Bayesian Optimization in the presence of constraints. Different acquisition functions are needed, and four examples are explored in more detail: constrained expected improvement, asymmetric...
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Conclusions

Pourmohamad, Tony; Lee, Herbert K. H.

Pages: 95 –96

[This short chapter provides a few concluding remarks.]

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Bayesian Optimization with Application to Computer Experiments

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