Elizabeth S. Meckes

Random Matrix Theory of the Classical Compact Groups (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This is the first book to provide a comprehensive overview of foundational results and recent progress in the study of random matrices from the classical compact groups, drawing on the subject's deep connections to geometry, analysis, algebra, physics, and statistics. The book sets a foundation with an introduction to the groups themselves and six different constructions of Haar measure. Classical and recent results are then presented in a digested, accessible form, including the following: results on the joint distributions of the entries; an extensive treatment of eigenvalue distributions, including the Weyl integration formula, moment formulae, and limit theorems and large deviations for the spectral measures; concentration of measure with applications both within random matrix theory and in high dimensional geometry; and results on characteristic polynomials with connections to the Riemann zeta function. This book will be a useful reference for researchers and an accessible introduction for students in related fields.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 1. August 2019
• Englisch
• ISBN-13: 9781108317993
• Artikelnr.: 62736827

Autorenporträt

Elizabeth S. Meckes is Professor of Mathematics at Case Western Reserve University, Ohio. She is a mathematical probabilist specializing in random matrix theory and its applications to other areas of mathematics, physics and statistics. She received her Ph.D. at Stanford University in 2006 and received the American Institute of Mathematics five-year fellowship. She has also received funding from the Clay Institute of Mathematics, the Simons Foundation, and the US National Science Foundation. She is the author of twenty-two research papers in mathematics, as well as the textbook Linear Algebra (Cambridge, 2018), co-authored with Mark Meckes.

Inhaltsangabe

1. Haar measure on the classical compact matrix groups
2. Distribution of the entries
3. Eigenvalue distributions: exact formulas
4. Eigenvalue distributions: asymptotics
5. Concentration of measure
6. Geometric applications of measure concentration
7. Characteristic polynomials and the zeta function.