Bayesian Optimization

Bayesian optimization is a methodology that has proven success in the sciences, engineering, and beyond for optimizing expensive objective functions. 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.

貝葉斯優化是一種在科學、工程等領域中優化昂貴目標函數的方法論，已經被證明成功。這本及時的書籍提供了一個自成一體且全面的介紹，從頭開始仔細發展所有關鍵的想法。這種自下而上的方法揭示了貝葉斯優化算法設計中的統一主題，並為應對新情況建立了堅實的理論基礎。本書的核心分為三個主要部分，涵蓋了高斯過程建模的理論和實踐方面、貝葉斯方法的序列決策以及實現和計算實用有效的優化策略。在這些基礎材料之後，本書提供了理論收斂結果的概述，重要擴展的調查，貝葉斯優化的全面歷史以及廣泛的應用文獻注釋。