Logo

Random Matrix Models and Their Applications

Large book cover: Random Matrix Models and Their Applications

Random Matrix Models and Their Applications
by

Publisher: Cambridge University Press
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.

Home page url

Download or read it online for free here:
Download link
(multiple PDF files)

Similar books

Book cover: Lectures on Noise Sensitivity and PercolationLectures on Noise Sensitivity and Percolation
by - 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.
(14465 views)
Book cover: Design of Comparative ExperimentsDesign of Comparative Experiments
by - Cambridge University Press
This book develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. The book is ideal for advanced undergraduate and beginning graduate courses.
(26228 views)
Book cover: Non-Uniform Random Variate GenerationNon-Uniform Random Variate Generation
by - Springer
The book on small field on the crossroads of statistics, operations research and computer science. The applications of random number generators are wide and varied. The study of non-uniform random variates is precisely the subject area of the book.
(17695 views)
Book cover: Bayesian Field TheoryBayesian Field Theory
by - arXiv.org
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
(9379 views)