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Springerbriefs in Optimization Ser.: Multiple Information Source Bayesian Optimization by Andrea Ponti, Francesco Archetti and Antonio Candelieri (2025, Trade Paperback)
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About this product
Product Identifiers
PublisherSpringer
ISBN-103031979648
ISBN-139783031979644
eBay Product ID (ePID)4079696400
Product Key Features
Number of PagesXii, 99 Pages
Publication NameMultiple Information Source Bayesian Optimization
LanguageEnglish
Publication Year2025
SubjectProbability & Statistics / General, Optimization, Probability & Statistics / Bayesian Analysis
TypeTextbook
Subject AreaMathematics
AuthorAndrea Ponti, Francesco Archetti, Antonio Candelieri
SeriesSpringerbriefs in Optimization Ser.
FormatTrade Paperback
Dimensions
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Number of Volumes1 vol.
IllustratedYes
Table Of ContentPreface.- Introduction.- MISO-AGP: dealing with multiple information sources via Augmented Gaussian Process.- MISO-AGP in action: selected applications.- Bayesian Optimization and Large Language Models.- References.
SynopsisThe 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 multiple information source approaches for solving real-world problems. Choosing the most appropriate strategy, depending on the specific problem features, ensures the success of the final solution. The book also offers an overview of available software tools: in particular it presents two implementations of the Augmented Gaussian Process-based Multiple Information Source Bayesian Optimization, one in Python -- and available as a development branch in BoTorch -- and finally, a comparative analysis against other available multi-fidelity and multiple information sources optimization tools is presented, considering both test problems and real-world applications. The book will be useful to two main audiences: 1. PhD candidates in Computer Science, Artificial Intelligence, Machine Learning, and Optimization 2. Researchers from academia and industry who want to implement effective and efficient procedures for designing experiments and optimizing computationally expensive experiments in domains like engineering design, material science, and biotechnology.
LC Classification NumberQA402.5-402.6
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