贝叶斯优化
计算机科学
贝叶斯概率
钥匙(锁)
高斯过程
实现(概率)
过程(计算)
人工智能
管理科学
机器学习
高斯分布
数学
工程类
统计
物理
计算机安全
量子力学
操作系统
标识
DOI:10.1017/9781108348973
摘要
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.
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