Table of contents
About these proceedings
High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, random graphs, information theory and convex geometry.
The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques and perspectives to analyze random phenomena.
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-26391-1
- Copyright Information Springer Nature Switzerland AG 2019
- Publisher Name Birkhäuser, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-030-26390-4
- Online ISBN 978-3-030-26391-1
- Series Print ISSN 1050-6977
- Series Online ISSN 2297-0428
- Buy this book on publisher's site