Bayesian Optimization

Bayesian optimization is a successful methodology for optimizing expensive objective functions across various fields. This book offers a comprehensive introduction, covering theoretical and practical aspects, with a focus on Gaussian process modeling, sequential decision-making, and practical optimization policies. It provides theoretical convergence results, surveys notable extensions, offers a history of Bayesian optimization, and includes an annotated bibliography of applications.

Format: Hardback
Length: 358 pages
Publication date: 09 February 2023
Publisher: Cambridge University Press

Bayesian optimization is a powerful methodology for optimizing complex objective functions across a wide range of fields, including science, engineering, and beyond. This comprehensive and timely text offers a thorough introduction to the subject, starting from the basics and gradually building upon key concepts. By taking a bottom-up approach, the book uncovers common themes in the design of Bayesian optimization algorithms and establishes a solid theoretical foundation for tackling new challenges.

The core of the book is organized into three main parts. The first part focuses on theoretical aspects of Gaussian process modeling, providing a comprehensive understanding of the underlying principles and techniques. The second part explores the Bayesian approach to sequential decision-making, highlighting its advantages and applications in various domains. The third part delves into the practical implementation and computation of optimization policies, showcasing real-world examples and strategies.

In addition to its theoretical foundation, the book provides an overview of theoretical convergence results, surveys notable extensions and applications, offers a comprehensive history of Bayesian optimization, and includes an extensive annotated bibliography of relevant applications. Whether you are a researcher, practitioner, or student interested in optimization, this book serves as a valuable resource for gaining a deeper understanding of Bayesian optimization and its applications in modern science and technology.

Weight: 1028g
Dimension: 259 x 211 x 24 (mm)
ISBN-13: 9781108425780