Overview
- Gives readers an idea of the potential of the application of Bayesian Optimization to both traditional feels and emerging ones
- Provides full and updated coverage of the areas of constrained Bayesian Optimization and Safe Bayesian Optimization
- Covers software resources, allowing readers to make informed and educated choices among the different platforms available to set up Bayesian Optimization components in academic and industrial activities
- Allows a full understanding of the basic algorithmic framework, including recent proposals about acquisition functions
Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)
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Table of contents (7 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Optimization and Data Science
Authors: Francesco Archetti, Antonio Candelieri
Series Title: SpringerBriefs in Optimization
DOI: https://doi.org/10.1007/978-3-030-24494-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-24493-4Published: 07 October 2019
eBook ISBN: 978-3-030-24494-1Published: 25 September 2019
Series ISSN: 2190-8354
Series E-ISSN: 2191-575X
Edition Number: 1
Number of Pages: XIII, 126
Number of Illustrations: 13 b/w illustrations, 39 illustrations in colour
Topics: Operations Research, Management Science, Machine Learning, Mathematical Software, Bayesian Inference