**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

**Introduction to Probability and Statistics Using R**

by

**G. Jay Kerns**

A textbook for an undergraduate course in probability and statistics. The prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.

(

**10703**views)

**Reversible Markov Chains and Random Walks on Graphs**

by

**David Aldous, James Allen Fill**-

**University of California, Berkeley**

From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.

(

**14857**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.

(

**14720**views)

**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.

(

**14108**views)