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.

Frontmatter Contents Preface 1 Introduction 2 gaussian processes 3 modeling with gaussian processes 4 model assessment, ion, 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