Binding: Hardcover
Description: 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.
Title: Bayesian Optimization
Author(s): Garnett Roman (Washington University In St Louis)
Publisher: Cambridge University Press
Barcode: 9781108425780
Pages: 358 Pages, Worked Examples Or Exercises
Publication Date: 3/23/2023
Category: Signal Processing