1 - Metric measure spaces
2 - Lie groups and matrix ensembles
3 - Entropy and concentration of measure
4 - Free entropy and equilibrium
5 - Convergence to equilibrium
6 - Gradient flows and functional inequalities
7 - Young tableaux
8 - Random point fields and random matrices
9 - Integrable operators and differential equations
10 - Fluctuations and the Tracy–Widom distribution
11 - Limit groups and Gaussian measures
12 - Hermite polynomials
13 - From the Ornstein–Uhlenbeck process to the Burgers equation
14 - Noncommutative probability spaces

This book focuses on the behaviour of large random matrices. Standard results are covered, and the presentation emphasizes elementary operator theory and differential equations, so as to be accessible to graduate students and other non-experts. The introductory chapters review material on Lie groups and probability measures in a style suitable for applications in random matrix theory. Later chapters use modern convexity theory to establish subtle results about the convergence of eigenvalue distributions as the size of the matrices increases. Random matrices are viewed as geometrical objects with large dimension. The book analyzes the concentration of measure phenomenon, which describes how measures behave on geometrical objects with large dimension. To prove such results for random matrices, the book develops the modern theory of optimal transportation and proves the associated functional inequalities involving entropy and information. These include the logarithmic Sobolev inequality, which measures how fast some physical systems converge to equilibrium. --- summary provided by publisher