Product details:
| ISBN13: | 9781108425780 |
| ISBN10: | 110842578X |
| Binding: | Hardback |
| No. of pages: | 358 pages |
| Size: | 261x209x22 mm |
| Weight: | 1020 g |
| Language: | English |
| 939 |
Category:
Bayesian Optimization
Publisher: Cambridge University Press
Date of Publication: 9 February 2023
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GBP 44.99
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Short description:
A comprehensive introduction to Bayesian optimization that starts from scratch and carefully develops all the key ideas along the way.
Long description:
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Table of Contents:
Notation; 1. Introduction; 2. Gaussian processes; 3. Modeling with Gaussian processes; 4. Model assessment, selection, and averaging; 5. Decision theory for optimization; 6. Utility functions for optimization; 7. Common Bayesian optimization policies; 8. Computing policies with Gaussian processes; 9. Implementation; 10. Theoretical analysis; 11. Extensions and related settings; 12. A brief history of Bayesian optimization; A. The Gaussian distribution; B. Methods for approximate Bayesian inference; C. Gradients; D. Annotated bibliography of applications; References; Index.
