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Random matrix theory has many roots and many branches in mathematics, statistics, physics, computer science, data science, numerical analysis, biology, ecology, engineering, and operations research. This book provides a snippet of this vast domain of study, with a particular focus on the notations of universality and integrability. Universality shows that many systems behave the same way in their large scale limit, while integrability provides a route to describe the nature of those universal limits. Many of the ten contributed chapters address these themes, while others touch on applications of tools and results from random matrix theory.
This book is appropriate for graduate students and researchers interested in learning techniques and results in random matrix theory from different perspectives and viewpoints. It also captures a moment in the evolution of the theory, when the previous decade brought major break-throughs, prompting exciting new directions of research.
Random matrix theory has many roots and many branches in mathematics, statistics, physics, computer science, data science, numerical analysis, biology, ecology, engineering, and operations research. This book provides a snippet of this vast domain of study, with a particular focus on the notation...
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關於作者
Alexei Borodin: Massachusetts Institute of Technology, Cambridge, MA
Ivan Corwin: Columbia University, New York, NY
Alice Guionnet: CNRS, ENS Lyon, Lyon, France
Alexei Borodin: Massachusetts Institute of Technology, Cambridge, MA
Ivan Corwin: Columbia University, New York, NY
Alice Guionnet: CNRS, ENS Lyon, Lyon, France
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