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 i...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Garnett, Roman ca. 20./21. Jh (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge, United Kingdom Cambridge University Press 2023
Schlagworte:	Bayesian statistical decision theory Gaussian processes Machine learning Bayes-Verfahren Gauß-Prozess Optimierung Bayes-Entscheidungstheorie
Online-Zugang:	DE-12 DE-634 DE-92 DE-863 DE-862 DE-29 Volltext
Zusammenfassung:	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
Beschreibung:	Title from publisher's bibliographic system (viewed on 25 Jan 2023)
Beschreibung:	1 Online-Ressource (xvi, 358 Seiten)
ISBN:	9781108348973
DOI:	10.1017/9781108348973