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Crista Arangala
exploring linear algebra: labs and projects with matlab(r) is a hands-on lab manual that can be used by students and instructors in classrooms every day to guide the exploration of the theory and applications of linear algebr.
Jeff Gill
The success of the first edition of generalized linear models led to the updated second edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data.
Daizhan Cheng
From dimension-free matrix theory to cross-dimensional dynamic systems illuminates the underlying mathematics of semi-tensor product (stp), a generalized matrix product that extends the conventional matrix product to two matrices of arbitrary dimen.
Howard Anton
elementary linear algebra 12th edition gives an elementary treatment of linear algebra that is suitable for a first course for undergraduate students.
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Frederick P. Greenleaf
Crista Arangala
exploring linear algebra: labs and projects with matlab(r) is a hands-on lab manual that can be used by students and instructors in classrooms every day to guide the exploration of the theory and applications of linear algebr.
D. R. Fulkerson
J. S. Chahal
Fundamentals of linear algebra is like no other book on the subject.
Tertulien Ndjountche
The book presents design methods for analog integrated circuits with improved electrical performance.
Wai-Kai Chen
Standard-setting, groundbreaking, authoritative, comprehensive--these often overused words perfectly describe the circuits and filters handbook, third edition.
Shankar P. Bhattacharyya
Kenneth G. Russell
Generalized linear models (glms) allow many statistical analyses to be extended to important statistical distributions other than the normal distribution.
J. S. Chahal
Fundamentals of linear algebra is like no other book on the subject.
Stephen H. Friedberg
Francesco Borrelli
Model predictive control (mpc), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book.
Tertulien Ndjountche
With a focus on designing and verifying cmos analog integrated circuits, the book reviews design techniques for mixed-signal building blocks, such as nyquist and oversampling data converters, and circuits for signal generation, synthesis, and recovery.
James W. Hardin
Generalized linear models and extensions, second edition provides a comprehensive overview of the nature and scope of generalized linear models (glms) and of the major changes to the basic glm algorithm that allow modeling of data that violate glm distrib.
Lin Weng
This book provides a systematic account of several breakthroughs in the modern theory of zeta functions.
James R. Kirkwood
Linear algebra is an important part of pure mathematics, and is needed for applications in every part of mathematics, natural science and economics.
Aleksandr Aravkin
Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.
Kenneth G. Russell
Generalized linear models (glms) allow many statistical analyses to be extended to important statistical distributions other than the normal distribution.
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Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposaluseful as a main resource or as a supplement in an economics or manageme.
J. S. Chahal
Fundamentals of linear algebra is like no other book on the subject.
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Tertulien Ndjountche
With a focus on designing and verifying cmos analog integrated circuits, the book reviews design techniques for mixed-signal building blocks, such as nyquist and oversampling data converters, and circuits for signal generation, synthesis, and recovery.
Daniele Bertaccini
This book describes, in a basic way, the most useful and effective iterative solvers and appropriate preconditioning techniques for some of the most important classes of large and sparse linear systems.
Kenneth G. Russell
Generalized linear models (glms) allow many statistical analyses to be extended to important statistical distributions other than the normal distribution.
J. S. Chahal
Fundamentals of linear algebra is like no other book on the subject.
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Since their introduction, hierarchical generalized linear models (hglms) have proven useful in various fields by allowing random effects in regression models.
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Youngjo Lee
Since their introduction, hierarchical generalized linear models (hglms) have proven useful in various fields by allowing random effects in regression models.
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invitation to linear algebra is an informative, clearly written, flexible textbook for instructors and students.
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This manual contains completely worked-out solutions for all the odd-numbered exercises in the text..
Francesco Borrelli
Model predictive control (mpc), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book.
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Note: this edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version.
Qingwen Hu
Concise introduction to linear algebra deals with the subject of linear algebra, covering vectors and linear systems, vector spaces, orthogonality, determinants, eigenvalues and eigenvectors, singular value decomposition.
Lawrence E. Spence
John F. Monahan
Willem Adriaan de Graaf
Designed as a self-contained account of a number of key algorithmic problems and their solutions for linear algebraic groups, this book combines in one single text both an introduction to the basic theory of linear algebraic groups and a substantial colle.
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This self-contained introduction to numerical linear algebra provides a comprehensive, yet concise, overview of the subject.
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Introduction to linear algebra, sixth edition, is a foundation text that bridges both practical computation and theoretical principles.
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Elementary linear algebra is written for the first undergraduate course.
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Jude Thaddeus Socrates
Marvin H. J. Gruber
Provides an easy-to-understand guide to statistical linear models and its uses in data analysis this book defines a broad spectrum of statistical linear models that is useful in the analysis of data.
Graham J. G. Upton
Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying r codethis book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicate.
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Matrix groups are a beautiful subject and are central to many fields in mathematics and physics.
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this book is the first to be devoted to the fusion between statistical causal inference and mathematical programming.
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Elementary linear algebra's clear, careful, and concise presentation of material helps you fully understand how mathematics works.