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Autor: James E. Gentle
ISBN-13: 9783319648668
Einband: Book
Seiten: 648
Gewicht: 1318 g
Format: 254x177x43 mm
Sprache: Englisch

Matrix Algebra

Springer Texts in Statistics
Theory, Computations and Applications in Statistics
90,94 €*
Comprehensive coverage of matrix algebra for data science and statistical theory
2.1.8 Orthogonal Vectors and Orthogonal Vector Spaces
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
Autor: James E. Gentle
¿James E. Gentle, PhD, is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. Professor Gentle has held several national offices in the ASA and has served as editor and 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 (Springer, 2003) and Computational Statistics (Springer, 2009).

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Autor: James E. Gentle
ISBN-13:: 9783319648668
ISBN: 3319648667
Erscheinungsjahr: 01.11.2017
Verlag: Springer-Verlag GmbH
Gewicht: 1318g
Seiten: 648
Sprache: Englisch
Auflage 17002, 2nd ed. 2017
Sonstiges: Taschenbuch, 254x177x43 mm, 36 schwarz-weiße Abbildungen, Bibliographie