DESCRIPTION

1st
Edition

by
Marc Potters (Author)

The
real world is perceived and broken down as data, models and algorithms in the
eyes of physicists and engineers. Data is noisy by nature and classical
statistical tools have so far been successful in dealing with relatively
smaller levels of randomness. The recent emergence of Big Data and the required
computing power to analyse them have rendered classical tools outdated and
insufficient. Tools such as random matrix theory and the study of large sample
covariance matrices can efficiently process these big data sets and help make
sense of modern, deep learning algorithms. Presenting an introductory calculus
course for random matrices, the book focusses on modern concepts in matrix
theory, generalising the standard concept of probabilistic independence to non-commuting
random variables. Concretely worked out examples and applications to financial
engineering and portfolio construction make this unique book an essential tool
for physicists, engineers, data analysts, and economists.

**DETAILS:**

**Year: **2021

**Pages: **370

**Language: **English

**Format: **PDF

**Size: **4 MB

**Publisher: **Cambridge University Press

**ISBN-10: **1108488080

**ISBN-13: **978-1108488082

**ASIN: **B08L9MKG9T

**Tag: **Download Book A First Course in Random Matrix
Theory: for Physicists, Engineers and Data Scientists