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|Matrices and Linear Algebra (Dover Books on Mathematics)
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List price: $17.95
Author: Hans Schneider
Linear algebra is one of the central disciplines in mathematics. A student of pure mathematics must know linear algebra if he is to continue with modern algebra or functional analysis. Much of the mathematics now taught to engineers and physicists requires it.
This well-known and highly regarded text makes the subject accessible to undergraduates with little mathematical experience. Written mainly for students in physics, engineering, economics, and other fields outside mathematics, the book gives the theory of matrices and applications to systems of linear equations, as well as many related topics such as determinants, eigenvalues, and differential equations.
Table of Contents:
l. The Algebra of Matrices
2. Linear Equations
3. Vector Spaces
5. Linear Transformations
6. Eigenvalues and Eigenvectors
7. Inner Product Spaces
8. Applications to Differential Equations
For the second edition, the authors added several exercises in each chapter and a brand new section in Chapter 7. The exercises, which are both true-false and multiple-choice, will enable the student to test his grasp of the definitions and theorems in the chapter. The new section in Chapter 7 illustrates the geometric content of Sylvester's Theorem by means of conic sections and quadric surfaces. 6 line drawings. lndex. Two prefaces. Answer section.
|Linear Models and the Relevant Distributions and Matrix Algebra (Chapman & Hall/CRC Texts in Statistical Science)
Lowest new price: $118.71
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Author: David A. Harville
Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical concepts in the context of a relatively complex setting of great practical importance.
- Provides coverage of matrix algebra that is extensive and relatively self-contained and does so in a meaningful context
- Provides thorough coverage of the relevant statistical distributions, including spherically and elliptically symmetric distributions
- Includes extensive coverage of multiple-comparison procedures (and of simultaneous confidence intervals), including procedures for controlling the k-FWER and the FDR
- Provides thorough coverage (complete with detailed and highly accessible proofs) of results on the properties of various linear-model procedures, including those of least squares estimators and those of the F test.
- Features the use of real data sets for illustrative purposes
- Includes many exercises
David Harville served for 10 years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories at Wright-Patterson AFB, Ohio, 20 years as a full professor in Iowa State University’s Department of Statistics where he now has emeritus status, and seven years as a research staff member of the Mathematical Sciences Department of IBM’s T.J. Watson Research Center. He has considerable relevant experience, having taught M.S. and Ph.D. level courses in linear models, been the thesis advisor of 10 Ph.D. graduates, and authored or co-authored two books and more than 80 research articles. His work has been recognized through his election as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and as a member of the International Statistical Institute.
|Fundamentals of Matrix Computations
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Author: David S. Watkins
Brand: Brand: Wiley
This new, modernized edition provides a clear and thorough introduction to matrix computations,a key component of scientific computing
Retaining the accessible and hands-on style of its predecessor, Fundamentals of Matrix Computations, Third Edition thoroughly details matrix computations and the accompanying theory alongside the author's useful insights. The book presents the most important algorithms of numerical linear algebra and helps readers to understand how the algorithms are developed and why they work.
Along with new and updated examples, the Third Edition features:
- A novel approach to Francis' QR algorithm that explains its properties without reference to the basic QR algorithm
- Application of classical Gram-Schmidt with reorthogonalization
- A revised approach to the derivation of the Golub-Reinsch SVD algorithm
- New coverage on solving product eigenvalue problems
- Expanded treatment of the Jacobi-Davidson method
- A new discussion on stopping criteria for iterative methods for solving linear equations
Throughout the book, numerous new and updated exercises—ranging from routine computations and verifications to challenging programming and proofs—are provided, allowing readers to immediately engage in applying the presented concepts. The new edition also incorporates MATLAB to solve real-world problems in electrical circuits, mass-spring systems, and simple partial differential equations, and an index of MATLAB terms assists readers with understanding the basic concepts related to the software.
Fundamentals of Matrix Computations, Third Edition is an excellent book for courses on matrix computations and applied numerical linear algebra at the upper-undergraduate and graduate level. The book is also a valuable resource for researchers and practitioners working in the fields of engineering and computer science who need to know how to solve problems involving matrix computations.
- Used Book in Good Condition
|Matrix Algebra Useful for Statistics (Wiley Series in Probability and Statistics)
Lowest new price: $101.94
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List price: $135.00
Author: Shayle R. Searle
A thoroughly updated guide to matrix algebra and it uses in statistical analysis and features SAS®, MATLAB®, and R throughout
This Second Edition addresses matrix algebra that is useful in the statistical analysis of data as well as within statistics as a whole. The material is presented in an explanatory style rather than a formal theorem-proof format and is self-contained. Featuring numerous applied illustrations, numerical examples, and exercises, the book has been updated to include the use of SAS, MATLAB, and R for the execution of matrix computations. In addition, André I. Khuri, who has extensive research and teaching experience in the field, joins this new edition as co-author. The Second Edition also:
- Contains new coverage on vector spaces and linear transformations and discusses computational aspects of matrices
- Covers the analysis of balanced linear models using direct products of matrices
- Analyzes multiresponse linear models where several responses can be of interest
- Includes extensive use of SAS, MATLAB, and R throughout
- Contains over 400 examples and exercises to reinforce understanding along with select solutions
- Includes plentiful new illustrations depicting the importance of geometry as well as historical interludes
Matrix Algebra Useful for Statistics, Second Edition is an ideal textbook for advanced undergraduate and first-year graduate level courses in statistics and other related disciplines. The book is also appropriate as a reference for independent readers who use statistics and wish to improve their knowledge of matrix algebra.
THE LATE SHAYLE R. SEARLE, PHD, was professor emeritus of biometry at Cornell University. He was the author of Linear Models for Unbalanced Data and Linear Models and co-author of Generalized, Linear, and Mixed Models, Second Edition, Matrix Algebra for Applied Economics, and Variance Components, all published by Wiley. Dr. Searle received the Alexander von Humboldt Senior Scientist Award, and he was an honorary fellow of the Royal Society of New Zealand.
ANDRÉ I. KHURI, PHD, is Professor Emeritus of Statistics at the University of Florida. He is the author of Advanced Calculus with Applications in Statistics, Second Edition and co-author of Statistical Tests for Mixed Linear Models, all published by Wiley. Dr. Khuri is a member of numerous academic associations, among them the American Statistical Association and the Institute of Mathematical Statistics.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
- Basics of Matrix Algebra for Statistics with R (Chapman & Hall/CRC The R Series)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Deep Learning (Adaptive Computation and Machine Learning series)
- Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
- Introduction to Probability (Chapman & Hall/CRC Texts in Statistical Science)
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
|Python: Programming: Your Step By Step Guide To Easily Learn Python in 7 Days (Python for Beginners, Python Programming for Beginners, Learn Python, Python Language)
Lowest new price: $16.95
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Author: iCode Academy
Are You Ready To Learn Python Easily? Learning Python Programming in 7 days is possible, although it might not look like it’s easy at first, especially if you are someone with meager experience in dealing with a programming language. As you learn, you will see that it is not that difficult to understand, and it becomes easier when you add patience, diligence, and discipline. This book has all the materials that you need in learning basic Python Programming. It is presented in easy to understand format – much of technical jargons were eliminated, although you might still find some, but they are the terms that you will likely (almost always) encounter when creating your codes. You will find no such trouble in understanding the terms, and how to do it properly. Samples are provided for you, and explanations are also presented so you won’t get lost and still have fun while learning. You will be glad that you have this book with you while learning Python. Discover many things that you can do with Python programming, and you might even start a new career.
Here’s What You’ll Learn From This Python For Beginners Book: ✓ Introduction ✓ Chapter 1: Welcome to the World of the Python ✓ Chapter 2: Python Syntax ✓ Chapter 3: Important Strings and Console Output ✓ Chapter 4: The Conditionals and Control Flow ✓ Chapter 5: Understanding Lists, Tuples, and Dictionaries ✓ Chapter 6: The Loops ✓ Chapter 7: Understanding the Functions ✓ Chapter 8: Coding a Full Program ✓ Chapter 9: Reserved Words and Functions ✓ and much more
What Are You Waiting For?
Start Coding Python Right Now!
|Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)
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Author: Gene H. Golub
Brand: Brand: Ashgate Pub Ltd
Revised and updated, the third edition of Golub and Van Loan's classic text in computer science provides essential information about the mathematical background and algorithmic skills required for the production of numerical software. This new edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of the modified Gram-Schmidt process, and new material devoted to GMRES, QMR, and other methods designed to handle the sparse unsymmetric linear system problem.
- Used Book in Good Condition
|Matrix Analysis for Statistics (Wiley Series in Probability and Statistics)
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Author: James R. Schott
An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice
Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms.
An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features:
• New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors
• Additional problems and chapter-end practice exercises at the end of each chapter
• Extensive examples that are familiar and easy to understand
• Self-contained chapters for flexibility in topic choice
• Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices
Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics.
James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schott’s research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques.
|Quaternions and Rotation Sequences
Lowest new price: $188.76
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List price: $92.50
Author: J. Kuipers
Brand: Princeton University Press
Ever since the Irish mathematician William Rowan Hamilton introduced quaternions in the nineteenth century--a feat he celebrated by carving the founding equations into a stone bridge--mathematicians and engineers have been fascinated by these mathematical objects. Today, they are used in applications as various as describing the geometry of spacetime, guiding the Space Shuttle, and developing computer applications in virtual reality. In this book, J. B. Kuipers introduces quaternions for scientists and engineers who have not encountered them before and shows how they can be used in a variety of practical situations.
The book is primarily an exposition of the quaternion, a 4-tuple, and its primary application in a rotation operator. But Kuipers also presents the more conventional and familiar 3 x 3 (9-element) matrix rotation operator. These parallel presentations allow the reader to judge which approaches are preferable for specific applications. The volume is divided into three main parts. The opening chapters present introductory material and establish the book's terminology and notation. The next part presents the mathematical properties of quaternions, including quaternion algebra and geometry. It includes more advanced special topics in spherical trigonometry, along with an introduction to quaternion calculus and perturbation theory, required in many situations involving dynamics and kinematics. In the final section, Kuipers discusses state-of-the-art applications. He presents a six degree-of-freedom electromagnetic position and orientation transducer and concludes by discussing the computer graphics necessary for the development of applications in virtual reality.
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|Linear Algebra and Matrix Analysis for Statistics (Chapman & Hall/CRC Texts in Statistical Science)
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Author: Sudipto Banerjee
Brand: CRC Press
Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra.
The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction.
The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.
|Finite-Dimensional Vector Spaces
Lowest new price: $12.99
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Author: Paul Halmos
Master expositor Paul Halmos presents Linear Algebra in the pure axiomatic spirit. He writes "My purpose in this book is to treat linear transformations on finite dimensional vector spaces by the methods of more general theories. The idea is to emphasize the simple geometric notions common to many parts of mathematics and its applications, and to do so in a language that gives away the trade secrets ...". This text is an ideal supplement to modern treatments of Linear Algebra. "The theory is systematically developed by the axiomatic method that has, since von Neumann, dominated the general approach to linear functional analysis and that achieves here a high degree of lucidity and clarity....The book contains about 350 well placed and instructive problems, which cover a considerable part of the subject. All in all this is an excellent work, of equally high value for both student and teacher." --Zentralblatt für Mathematik.
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