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Bayesian Optimization by Roman Garnett (2023, Hardcover)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-10110842578X

ISBN-139781108425780

eBay Product ID (ePID)13057247613

Product Key Features

Number of Pages150 Pages

Publication NameBayesian Optimization

LanguageEnglish

Publication Year2023

SubjectGeneral, Computer Vision & Pattern Recognition

TypeTextbook

AuthorRoman Garnett

Subject AreaMathematics, Computers

FormatHardcover

Dimensions

Item Height0.9 in

Item Length10.3 in

Item Width8.2 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2022-032075

Dewey Edition23

IllustratedYes

Dewey Decimal519.542

Table Of ContentNotation; 1. Introduction; 2. Gaussian processes; 3. Modeling with Gaussian processes; 4. Model assessment, selection, 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.

SynopsisBayesian 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., Bayesian optimization is a methodology that has proven success in the sciences, engineering, and beyond for optimizing expensive objective functions. This self-contained text targets graduate students and researchers in machine learning and statistics - and practitioners from other fields - wishing to harness the power of Bayesian optimization.

LC Classification NumberQA279.5.G37 2023