000			01682nam a22002177a 4500
005			20241204154513.0
008			241204b |||||||| |||| 00| 0 eng d
020			_a9781108425780
082			_a519.542 _bGAR
100			_aGarnett, Roman _918180
245			_aBayesian optimization
260			_bCambridge University Press _aNew York _c2023
300			_axvi, 258 p.
365			_aGBP _b44.99
520			_aBayesian 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)
650			_aMachine learning
650			_aMathematics
650			_aComputer Science
650			_aPattern Recognition _919319
942			_cBK _2ddc
999			_c7639 _d7639