9783031979644 - multiple information source bayesian optimization di candelieri, antonio; ponti, andrea; archetti, francesco (16 risultati)
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condizione: New. Multiple Information Source Bayesian Optimization. Book.
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 62,45
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Condizione: As New. Unread book in perfect condition.
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 75,26
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Condizione: New.
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 66,05
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Condizione: New.
Multiple Information Source Bayesian Optimization
Candelieri, Antonio; Ponti, Andrea; Archetti, Francesco; Sabatella, Antonio (CON)
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 66,50
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Condizione: As New. Unread book in perfect condition.
Multiple Information Source Bayesian Optimization
Candelieri, Antonio/ Ponti, Andrea/ Archetti, Francesco/ Sabatella, Antonio (Contributor)
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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EUR 75,67
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Paperback. Condizione: Brand New. 111 pages. 9.26x6.11x9.21 inches. In Stock.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 53,49
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Taschenbuch. Condizione: 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…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.
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: 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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- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Berlin, Springer Nature Switzerland, Springer 2025
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- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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 re…lations 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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- Print on Demand
Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
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EUR 75,54
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Condizione: New. Print on Demand.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
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- Print on Demand
Da: moluna, Greven, , Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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EUR 66,06
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Paperback. Condizione: 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.
Lingua: Inglese
Editore: Springer, Springer International Publishing Aug 2025 2025
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 53,49
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: 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 relati…ons 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.
