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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.

Machine learning. --- Computer software. --- Statistics. --- Operations Research, Management Science. --- Machine Learning. --- Mathematical Software. --- Bayesian Inference. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Software, Computer --- Computer systems --- Learning, Machine --- Artificial intelligence --- Machine theory --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Operations research. --- Management science. --- Statistics . --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory

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• Reference Manager
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