**Random Matrix Models and Their Applications**

by Pavel Bleher, Alexander Its

**Publisher**: Cambridge University Press 2001**ISBN/ASIN**: 0521802091**ISBN-13**: 9780521802093**Number of pages**: 438

**Description**:

The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems. Its focus on the interaction between physics and mathematics will make it a welcome addition to the shelves of graduate students and researchers in both fields, as will its expository emphasis.

Download or read it online for free here:

**Download link**

(multiple PDF,PS files)

## Similar books

**An Introduction to Stochastic PDEs**

by

**Martin Hairer**-

**arXiv**

This text is an attempt to give a reasonably self-contained presentation of the basic theory of stochastic partial differential equations, taking for granted basic measure theory, functional analysis and probability theory, but nothing else.

(

**9549**views)

**Lectures on Probability, Statistics and Econometrics**

by

**Marco Taboga**-

**statlect.com**

This e-book is organized as a website that provides access to a series of lectures on fundamentals of probability, statistics and econometrics, as well as to a number of exercises on the same topics. The level is intermediate.

(

**9418**views)

**Markov Chains and Mixing Times**

by

**D. A. Levin, Y. Peres, E. L. Wilmer**-

**American Mathematical Society**

An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.

(

**9748**views)

**Probability, Statistics and Stochastic Processes**

by

**Cosma Rohilla Shalizi**

Contents: Probability (Probability Calculus, Random Variables, Discrete and Continuous Distributions); Statistics (Handling of Data, Sampling, Estimation, Hypothesis Testing); Stochastic Processes (Markov Processes, Continuous-Time Processes).

(

**7443**views)