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 â and practitioners from other fields â wishing to harness the power of Bayesian optimization.
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 â and practitioners from other fields â wishing to harness the power of Bayesian optimization.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Roman Garnett is Associate Professor at Washington University in St. Louis. He has been a leader in the Bayesian optimization community since 2011, when he co-founded a long-running workshop on the subject at the NeurIPS conference. His research focus is developing Bayesian methods - including Bayesian optimization - for automating scientific discovery, an effort supported by an NSF CAREER award.
Inhaltsangabe
Notation 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.
Notation 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.
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