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
Lectures on Noise Sensitivity and Percolationby Christophe Garban, Jeffrey E. Steif - arXiv
The goal of this set of lectures is to combine two seemingly unrelated topics: (1) The study of Boolean functions, a field particularly active in computer science; (2) Some models in statistical physics, mostly percolation.
(12526 views)
CK-12 Basic Probability and Statistics: A Short Courseby Brenda Meery - CK-12.org
CK-12 Foundation's Basic Probability and Statistics– A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
(21270 views)
Think Stats: Probability and Statistics for Programmersby Allen B. Downey - Green Tea Press
Think Stats is an introduction to Probability and Statistics for Python programmers. This new book emphasizes simple techniques you can use to explore real data sets and answer interesting statistical questions. Basic skills in Python are assumed.
(23497 views)
Markov Chains and Stochastic Stabilityby S.P. Meyn, R.L. Tweedie - Springer
The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.
(22230 views)