Bayesian optimization

By:

Garnett, Roman

Material type: Text

TextPublication details: Cambridge University Press New York 2023Description: xvi, 258 pISBN:

9781108425780

Subject(s):

DDC classification:

519.542 GAR

Summary: 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. (https://www.cambridge.org/core/books/bayesian-optimization/11AED383B208E7F22A4CE1B5BCBADB44#fndtn-information)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.542 GAR (Browse shelf(Opens below))	1	Available		006753

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Previous	519.54 KIM Statistical methods for handling incomplete data	519.54 MAV Probability and statistical inference:	519.542 DON Bayesian statistics for beginners: a step-by-step approach	519.542 GAR Bayesian optimization	519.542 LIO Bayesian inference and computation in reliability and survival analysis	519.542 LON Statistics for making decisions	519.542 MA Bayesian models of perception and action: an introduction	Next

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.
(https://www.cambridge.org/core/books/bayesian-optimization/11AED383B208E7F22A4CE1B5BCBADB44#fndtn-information)

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