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.
- A comprehensive introduction that includes almost 200 figures and extensive marginal notes, conveying the intuition behind difficult concepts
- Features an extensive annotated bibliography of applications, demonstrating the challenges inherent to the myriad fields where Bayesian optimization has proven successful and how they were overcome
- Compares common Bayesian optimization policies on a running optimization problem, allowing readers to identify nuances in behavior among available algorithms
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Product details
- Date Published: February 2023
- format: Hardback
- isbn: 9781108425780
- length: 358 pages
- dimensions: 261 x 209 x 22 mm
- weight: 1.02kg
- availability: Available
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.
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Author
Roman Garnett, Washington University in St Louis
Roman Garnett is Associate Professor at Washington University in St. Louis. He has been a leader in the Bayesian optimization community since 2011, when he co-founded a long-running workshop on the subject at the NeurIPS conference. His research focus is developing Bayesian methods – including Bayesian optimization – for automating scientific discovery, an effort supported by an NSF CAREER award.

This title is available for institutional purchase via Cambridge Core

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Bayesian Optimization

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