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Multiple Information Source Bayesian Optimization (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031979648 ISBN 13: 9783031979644
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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
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Multiple Information Source Bayesian Optimization
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 77.91
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Bayesian Optimization and Data Science (SpringerBriefs in Optimization)
Book 36 of 43: SpringerBriefs in OptimizationSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Paperback or Softback. Condition: New. Multiple Information Source Bayesian Optimization. Book.
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Multiple Information Source Bayesian Optimization
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 78.48
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Bayesian Optimization and Data Science
Book 36 of 43: SpringerBriefs in OptimizationSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 82.82
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US$ 88.94
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Add to basketPaperback. Condition: Brand New. 111 pages. 9.26x6.11x9.21 inches. In Stock.
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Condition: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.
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Bayesian Optimization and Data Science
Book 36 of 43: SpringerBriefs in OptimizationSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Add to basketPaperback. Condition: Brand New. 126 pages. 9.25x6.25x0.50 inches. In Stock.
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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 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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Bayesian Optimization and Data Science
Book 36 of 43: SpringerBriefs in OptimizationLanguage: English
Published by Springer International Publishing, Springer International Publishing, 2019
ISBN 10: 3030244938 ISBN 13: 9783030244934
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
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Condition: new. Questo è un articolo print on demand.
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Multiple Information Source Bayesian Optimization
Language: English
Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10: 3031979648 ISBN 13: 9783031979644
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
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 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. 99 pp. Englisch.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Bayesian Optimization and Data Science
Book 36 of 43: SpringerBriefs in OptimizationLanguage: English
Published by Springer International Publishing Okt 2019, 2019
ISBN 10: 3030244938 ISBN 13: 9783030244934
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities. 140 pp. Englisch.
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Multiple Information Source Bayesian Optimization
Language: English
Published by Springer Verlag GmbH, 2025
ISBN 10: 3031979648 ISBN 13: 9783031979644
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Bayesian Optimization and Data Science (SpringerBriefs in Optimization)
Book 36 of 43: SpringerBriefs in OptimizationUS$ 105.07
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Ships from United Kingdom to U.S.A.Quantity: 4 available
Add to basketCondition: New. Print on Demand.
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Multiple Information Source Bayesian Optimization (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031979648 ISBN 13: 9783031979644
US$ 77.92
US$ 50.45 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. 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.
-
Condition: New. PRINT ON DEMAND.
-
Multiple Information Source Bayesian Optimization (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031979648 ISBN 13: 9783031979644
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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
