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Statistical Optimal Transport : École d'Été de Probabilités de Saint-Flour XLIX – 2019 / by Sinho Chewi, Jonathan Niles-Weed, Philippe Rigollet.
Springer Nature - Springer Mathematics and Statistics (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Author/Creator:
- Chewi, Sinho., Author.
- Niles-Weed, Jonathan., Author.
- Rigollet, Philippe., Author.
- Series:
- École d'Été de Probabilités de Saint-Flour ; 2364
- Language:
- English
- Subjects (All):
- Statistics.
- Machine learning.
- Mathematical optimization.
- Calculus of variations.
- Statistical physics.
- Probabilities.
- Statistical Theory and Methods.
- Machine Learning.
- Calculus of Variations and Optimization.
- Statistical Physics.
- Probability Theory.
- Local Subjects:
- Statistical Theory and Methods.
- Machine Learning.
- Calculus of Variations and Optimization.
- Statistical Physics.
- Probability Theory.
- Physical Description:
- 1 online resource (XIV, 260 p. 10 illus., 3 illus. in color.)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- This monograph aims to offer a concise introduction to optimal transport, quickly transitioning to its applications in statistics and machine learning. It is primarily tailored for students and researchers in these fields, yet it remains accessible to a broader audience of applied mathematicians and computer scientists. Each chapter is complemented with exercises for the reader to test their understanding. As such, this monograph is suitable for a graduate course on the topic of statistical optimal transport.
- Contents:
- 1. Optimal Transport
- 2. Estimation of Wasserstein distances
- 3. Estimation of transport maps
- 4. Entropic optimal transport
- 5. Wasserstein gradient flows: theory.-6. Wasserstein gradient flows: applications
- 7. Metric geometry of the Wasserstein space
- 8. Wasserstein barycenters.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-85160-9
- OCLC:
- 1523376299
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