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Graph Notebook 1/2 inch Squares: Blank Quad Ruled 110 Square Grid Pages Large (8.5” x 11”) (Composition Books)
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Author: Studio Kids JK

Graph Paper Notebook 1/2 inch Squares features pages covered with a continuous square grid. The squares have different sizes according to your preference. Perfect Graph Paper Notebook, Graph Paper Journal / Graph Paper, size with its 8.5" x 11" dimensions and has a high quality soft cover. Perfect for Kids and Teens.
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Group Inverses of MMatrices and Their Applications (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science)
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Author: Stephen J. Kirkland
Brand: Brand: Chapman and Hall/CRC

Group inverses for singular Mmatrices are useful tools not only in matrix analysis, but also in the analysis of stochastic processes, graph theory, electrical networks, and demographic models. Group Inverses of MMatrices and Their Applications highlights the importance and utility of the group inverses of Mmatrices in several application areas. After introducing sample problems associated with Leslie matrices and stochastic matrices, the authors develop the basic algebraic and spectral properties of the group inverse of a general matrix. They then derive formulas for derivatives of matrix functions and apply the formulas to matrices arising in a demographic setting, including the class of Leslie matrices. With a focus on Markov chains, the text shows how the group inverse of an appropriate Mmatrix is used in the perturbation analysis of the stationary distribution vector as well as in the derivation of a bound for the asymptotic convergence rate of the underlying Markov chain. It also illustrates how to use the group inverse to compute and analyze the mean first passage matrix for a Markov chain. The final chapters focus on the Laplacian matrix for an undirected graph and compare approaches for computing the group inverse. Collecting diverse results into a single volume, this selfcontained book emphasizes the connections between problems arising in Markov chains, Perron eigenvalue analysis, and spectral graph theory. It shows how group inverses offer valuable insight into each of these areas.
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Matrix Algebra Useful for Statistics (Wiley Series in Probability and Statistics)
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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 theoremproof format and is selfcontained. 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 coauthor. 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 firstyear 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 coauthor 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 coauthor 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.
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 An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
 Deep Learning (Adaptive Computation and Machine Learning)
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 Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
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 Basics of Matrix Algebra for Statistics with R (Chapman & Hall/CRC The R Series)
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 Matrix Analysis for Statistics (Wiley Series in Probability and Statistics)
 Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition


Sparsity: Graphs, Structures, and Algorithms (Algorithms and Combinatorics)
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Author: Jaroslav Nesetril
Brand: Brand: Springer

This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a hard to define notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties. For example the classification is expressible in many different ways involving most extremal combinatorial invariants. This study of sparse structures found applications in such diverse areas as algorithmic graph theory, complexity of algorithms, property testing, descriptive complexity and mathematical logic (homomorphism preservation,fixed parameter tractability and constraint satisfaction problems). It should be stressed that despite of its generality this approach leads to linear (and nearly linear) algorithms.
Jaroslav Nešetřil is a professor at Charles University, Prague; Patrice Ossona de Mendez is a CNRS researcher et EHESS, Paris. This book is related to the material presented by the first author at ICM 2010.
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 Used Book in Good Condition


Fundamentals of Matrix Computations
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Author: David S. Watkins
Brand: Brand: Wiley

This new, modernized edition provides a clear and thoroughintroduction to matrix computations,a key component of scientificcomputing Retaining the accessible and handson style of its predecessor,Fundamentals of Matrix Computations, Third Editionthoroughly details matrix computations and the accompanying theoryalongside the author's useful insights. The book presents the mostimportant algorithms of numerical linear algebra and helps readersto understand how the algorithms are developed and why theywork. Along with new and updated examples, the Third Editionfeatures:  A novel approach to Francis' QR algorithm that explains itsproperties without reference to the basic QR algorithm
 Application of classical GramSchmidt withreorthogonalization
 A revised approach to the derivation of the GolubReinsch SVDalgorithm
 New coverage on solving product eigenvalue problems
 Expanded treatment of the JacobiDavidson method
 A new discussion on stopping criteria for iterative methods forsolving linear equations
Throughout the book, numerous new and updatedexercises—ranging from routine computations and verificationsto challenging programming and proofs—are provided, allowingreaders to immediately engage in applying the presented concepts.The new edition also incorporates MATLAB to solve realworldproblems in electrical circuits, massspring systems, and simplepartial differential equations, and an index of MATLAB termsassists readers with understanding the basic concepts related tothe software. Fundamentals of Matrix Computations, Third Edition is anexcellent book for courses on matrix computations and appliednumerical linear algebra at the upperundergraduate and graduatelevel. The book is also a valuable resource for researchers andpractitioners working in the fields of engineering and computerscience who need to know how to solve problems involving matrixcomputations.
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 Used Book in Good Condition
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I'm a Math Teacher Of Course I Have Problems: Journal with Lined and Blank Pages for Funny Math Teacher Appreciation Gift
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Author: Teacher Appreciation Quotes and Gifts

Show an awesome Math Teacher how much you appreciate their hard work with this funny Math Teacher quote. This journal has half lightly lined pages and half blank pages  perfect for classroom notes, lists, math problems, ideas or doodles. Features: Lines on one side, blank on the opposite side Soft matte cover with blackboard and chalk funny teacher quote. Size is 6x9 perfect for purses, bags or desks. This under ten dollar gift for teachers is a perfect for: Math Teacher appreciation week gift End of year teacher gift Teacher Christmas gift Teacher gift for men Teacher gift for women Mom or Dad Math Teacher math professor gifts Math Teacher Gifts under $10 Math Teacher retirement gifts Your math teacher will appreciate this thoughtful notebook. Please don't give your math teacher yet another mug or candy! Give them something practical and memorable! Cover: Chalkboard background with Chalk quote: "I'm a Math Teacher of Course I Have Problems" with Math Problems surrounding the funny math quote.
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Matrices and Linear Transformations: Second Edition (Dover Books on Mathematics)
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Author: Charles G. Cullen

"Comprehensive . . . an excellent introduction to the subject." — Electronic Engineer's Design Magazine. This introductory textbook, aimed at sophomore and juniorlevel undergraduates in mathematics, engineering, and the physical sciences, offers a smooth, indepth treatment of linear algebra and matrix theory. The major objects of study are matrices over an arbitrary field. Contents include Matrices and Linear Systems; Vector Spaces; Determinants; Linear Transformations; Similarity: Part I and Part II; Polynomials and Polynomial Matrices; Matrix Analysis; and Numerical Methods. The first seven chapters, which require only a first course in calculus and analytic geometry, deal with matrices and linear systems, vector spaces, determinants, linear transformations, similarity, polynomials, and polynomial matrices. Chapters 8 and 9, parts of which require the student to have completed the normal course sequence in calculus and differential equations, provide introductions to matrix analysis and numerical linear algebra, respectively. Among the key features are coverage of spectral decomposition, the Jordan canonical form, the solution of the matrix equation AX = XB, and over 375 problems, many with answers.
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Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
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Author: Arup Bose

Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in highdimensional models and novel ideas on how to use them for statistical inference in one or more highdimensional time series models. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis and basic results in stochastic convergence. Part I is on different methods of estimation of large covariance matrices and autocovariance matrices and properties of these estimators. Part II covers the relevant material on random matrix theory and noncommutative probability. Part III provides results on limit spectra and asymptotic normality of traces of symmetric matrix polynomial functions of sample autocovariance matrices in highdimensional linear time series models. These are used to develop graphical and significance tests for different hypotheses involving one or more independent highdimensional linear time series. The book should be of interest to people in econometrics and statistics (large covariance matrices and highdimensional time series), mathematics (random matrices and free probability) and computer science (wireless communication). Parts of it can be used in postgraduate courses on highdimensional statistical inference, highdimensional random matrices and highdimensional time series models. It should be particularly attractive to researchers developing statistical methods in highdimensional time series models. Arup Bose is a professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in mathematical statistics and has been working in highdimensional random matrices for the last fifteen years. He has been editor of Sankhyā for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His first book Patterned Random Matrices was also published by Chapman & Hall. He has a forthcoming graduate text Ustatistics, Mestimates and Resampling (with Snigdhansu Chatterjee) to be published by Hindustan Book Agency. Monika Bhattacharjee is a postdoctoral fellow at the Informatics Institute, University of Florida. After graduating from St. Xavier's College, Kolkata, she obtained her master’s in 2012 and PhD in 2016 from the Indian Statistical Institute. Her thesis in highdimensional covariance and autocovariance matrices, written under the supervision of Dr. Bose, has received high acclaim.


Modern Matrix Algebra
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Author: David R. Hill

This book presents the basic ideas of matrix and linear algebra in such a way that users from diverse backgrounds (who have had some exposure to calculus) will understand, by utilizing both algebraic and geometric reasoning. A spiral approach gradually introduces the abstract foundations of the topics involved—linear combination, closure, subspaces, linear independence/dependence, and bases. Opportunities for a variety of applications, and the optional use of MATLAB, provide handson explorations of computations and concepts. Chapter topics include matrices, linear systems and their solutions, Eigen information, vector spaces, inner product spaces, and linear transformations. For individuals who want to learn abstract concepts and deal with a wide variety of applications that can be drawn from fields such as physics, chemistry, biology, geology, economics, engineering, computer science, psychology, and sociology.


Fundamentals of MatrixAnalytic Methods
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Author: QiMing He

Fundamentals of MatrixAnalytic Methods targets advancedlevel students in mathematics, engineering and computer science. It focuses on the fundamental parts of MatrixAnalytic Methods, PhaseType Distributions, Markovian arrival processes and Structured Markov chains and matrix geometric solutions. New materials and techniques are presented for the first time in research and engineering design. This book emphasizes stochastic modeling by offering probabilistic interpretation and constructive proofs for MatrixAnalytic Methods. Such an approach is especially useful for engineering analysis and design. Exercises and examples are provided throughout the book.


