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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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