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Bayesian optimization and data science

Archetti, Francesco.

Bayesian optimization and data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian optimization and data scienceby Francesco Archetti, Antonio Candelieri.
Author:	Archetti, Francesco.
other author:	Candelieri, Antonio.
Published:	Cham :Springer International Publishing :2019.
Description:	xiii, 126 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theory.
Online resource:	https://doi.org/10.1007/978-3-030-24494-1
ISBN:	9783030244941$q(electronic bk.)

Bayesian optimization and data science
Archetti, Francesco.

Bayesian optimization and data science[electronic resource] /by Francesco Archetti, Antonio Candelieri. - Cham :Springer International Publishing :2019. - xiii, 126 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in optimization,2190-8354. - SpringerBriefs in optimization..

1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

ISBN: 9783030244941$q(electronic bk.)

Standard No.: 10.1007/978-3-030-24494-1doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .A734 2019

Dewey Class. No.: 519.542

Bayesian optimization and data science
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260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiii, 126 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in optimization, $x 2190-8354
505 0 $a 1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications.
520 $a This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
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650 2 4 $a Bayesian Inference. $3 825978
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950 $a Mathematics and Statistics (Springer-11649)