书目名称 | Matrix Algebra |
副标题 | Theory, Computations |
编辑 | James E. Gentle |
视频video | |
概述 | Comprehensive coverage of matrix algebra for data science and statistical theory.Over 100 pages of additional material and 30 extra exercises in the new edition.Even clearer text and more comprehensiv |
丛书名称 | Springer Texts in Statistics |
图书封面 |  |
描述 | .This textbook for graduate and advanced undergraduate students presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and the second edition of this very popular textbook provides essential updates and comprehensive coverage on critical topics in mathematics in data science and in statistical theory. .Part I offers a self-contained description of relevant aspects of the theory of matrix algebra for applications in statistics. It begins with fundamental concepts of vectors and vector spaces; covers basic algebraic properties of matrices and analytic properties of vectors and matrices in multivariate calculus; and concludes with a discussion on operations on matrices in solutions of linear systems and in eigenanalysis. Part II considers various types of matricesencountered in statistics, such as projection matrices and positive definite matrices, and describes special properties of those matrices; and describes various applications of matrix theory in statistics, inc |
出版日期 | Textbook 20172nd edition |
关键词 | matrix; linear algebra; numerical analysis; optimization; linear model; vector; linear transformation; sing |
版次 | 2 |
doi | https://doi.org/10.1007/978-3-319-64867-5 |
isbn_softcover | 978-3-319-64866-8 |
isbn_ebook | 978-3-319-64867-5Series ISSN 1431-875X Series E-ISSN 2197-4136 |
issn_series | 1431-875X |
copyright | Springer International Publishing AG 2017 |