Multiple Information Source Bayesian by Candelieri Antonio (17 results)
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
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Paperback or Softback. Condition: New. Multiple Information Source Bayesian Optimization. Book.
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
- Softcover
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Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Multiple Information Source Bayesian Optimization
Candelieri, Antonio/ Ponti, Andrea/ Archetti, Francesco/ Sabatella, Antonio (Contributor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 111 pages. 9.26x6.11x9.21 inches. In Stock.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 i…mportant 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 Optimization2. 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, andbiotechnology.
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Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Multiple Information Source Bayesian Optimization | Antonio Candelieri (u. a.) | Taschenbuch | SpringerBriefs in Optimization | xii | Englisch | 2025 | Springer | EAN 9783031979644 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]…springer[dot]com | Anbieter: preigu.
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
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Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
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Language: English
Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2025
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 rel…ations 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 Optimization2. 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, andbiotechnology. 99 pp. Englisch.
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Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Language: English
Published by Springer International Publishing AG, Cham, 2025
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. 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 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 Optimization2. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Language: English
Published by Springer, Springer International Publishing Aug 2025, 2025
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 relatio…ns 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 Optimization2. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 112 pp. Englisch.
