Comprehensive coverage of matrix algebra for data science and statistical theoryOver 100 pages of additional material and 30 extra exercises in the new editionEven clearer text and more comprehensive coverage

Autorentext

James E. Gentle is University Professor Emeritus at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA, as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods, Computational Statistics, and Statistical Analysis of Financial Data. He is co-editor-in-chief of Wiley Interdisciplinary Reviews: Computational Statistics.

Inhalt

Part I Linear Algebra.- 1 Basic Vector/Matrix Structure and Notation.- 2 Vectors and Vector Spaces.- 3 Basic Properties of Matrices.- 4 Vector/Matrix Derivatives and Integrals.- 5 Matrix Transformations and Factorizations.- 6 Solution of Linear Systems.- 7 Evaluation of Eigenvalues and Eigenvectors.