Bayesian Optimization

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