Multiple Information Source Bayesian Optimization

The book provides a comprehensive review of multiple information sources and multi-fidelity Bayesian optimization, specifically focusing on the novel "Augmented Gaussian Process” methodology. The book is important to clarify the relations and the important differences in using multi-fidelity or...

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Bibliographic Details

Main Authors:	Candelieri, Antonio (Author), Ponti, Andrea (Author), Archetti, Francesco (Author)
Corporate Author:	SpringerLink (Online service)
Format:	Electronic eBook
Language:	English
Published:	Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edition:	1st ed. 2025.
Series:	SpringerBriefs in Optimization,
Subjects:	Mathematical optimization. Statistics . Machine learning. Optimization. Bayesian Inference. Machine Learning.
Online Access:	https://doi.org/10.1007/978-3-031-97965-1
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