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Invitation to Linear Operators From Matrices to Bounded Linear Operators on a Hilbert Space

Invitation to Linear Operators From Matrices to Bounded Linear Operators on a Hilbert Space

Most books on linear operators are not easy to follow for students and researchers without an extensive background in mathematics. Self-contained and using only matrix theory Invitation to Linear Operators: From Matricies to Bounded Linear Operators on a Hilbert Space explains in easy-to-follow steps a variety of interesting recent results on linear operators on a Hilbert space. The author first states the important properties of a Hilbert space then sets out the fundamental properties of bounded linear operators on a Hilbert space. The final section presents some of the more recent developments in bounded linear operators. | Invitation to Linear Operators From Matrices to Bounded Linear Operators on a Hilbert Space

GBP 175.00
1

Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
1

A Primer on Linear Models

A Primer on Linear Models

A Primer on Linear Models presents a unified thorough and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity the text first provides examples of the general linear model including multiple regression models one-way ANOVA mixed-effects models and time series models. It then introduces the basic algebra and geometry of the linear least squares problem before delving into estimability and the Gauss Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals the author analyzes mixed models such as two-way mixed ANOVA and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general unifying approach to the theory fundamentals and exact results of linear models.

GBP 175.00
1

Differential Equations A Linear Algebra Approach

Differential Equations A Linear Algebra Approach

Differential Equations: A Linear Algebra Approach follows an innovative approach of inculcating linear algebra and elementary functional analysis in the backdrop of even the simple methods of solving ordinary differential equations. The contents of the book have been made user-friendly through concise useful theoretical discussions and numerous illustrative examples practical and pathological. | Differential Equations A Linear Algebra Approach

GBP 105.00
1

A First Course in Linear Model Theory

A First Course in Linear Model Theory

Thoroughly updated throughout A First Course in Linear Model Theory Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality this second edition features three new chapters and several sections on new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include linear fixed random and mixed effects models generalized linear models Bayesian and hierarchical linear models model selection multiple comparisons and regularized and robust regression. New to the Second Edition: Coverage of inference for linear models has been expanded into two chapters. Expanded coverage of multiple comparisons random and mixed effects models model selection and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13 and expanded coverage of the Bayesian linear models and longitudinal models. A new section on regularized regression in Chapter 14. Detailed data illustrations using R. The authors' fresh approach methodical presentation wealth of examples use of R and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models generalized linear models nonlinear models and dynamic models.

GBP 82.99
1

Concise Introduction to Linear Algebra

Concise Introduction to Linear Algebra

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. It adopts an efficient approach to lead students from vectors matrices quickly into more advanced topics including LU decomposition orthogonal decomposition Least squares solutions Gram-Schmidt process eigenvalues and eigenvectors diagonalizability spectral decomposition positive definite matrix quadratic forms singular value decompositions and principal component analysis. This book is designed for onesemester teaching to undergraduate students.

GBP 44.99
1

Introduction to Linear Algebra

Introduction to Linear Algebra

Linear algebra provides the essential mathematical tools to tackle all the problems in Science. Introduction to Linear Algebra is primarily aimed at students in applied fields (e. g. Computer Science and Engineering) providing them with a concrete rigorous approach to face and solve various types of problems for the applications of their interest. This book offers a straightforward introduction to linear algebra that requires a minimal mathematical background to read and engage with. Features Presented in a brief informative and engaging style Suitable for a wide broad range of undergraduates Contains many worked examples and exercises

GBP 44.99
1

Non-Linear Differential Equations and Dynamical Systems

Non-Linear Differential Equations and Dynamical Systems

Non-Linear Differential Equations and Dynamical Systems is the second book within Ordinary Differential Equations with Applications to Trajectories and Vibrations Six-volume Set. As a set they are the fourth volume in the series Mathematics and Physics Applied to Science and Technology. This second book consists of two chapters (chapters 3 and 4 of the set). The first chapter considers non-linear differential equations of first order including variable coefficients. A first-order differential equation is equivalent to a first-order differential in two variables. The differentials of order higher than the first and with more than two variables are also considered. The applications include the representation of vector fields by potentials. The second chapter in the book starts with linear oscillators with coefficients varying with time including parametric resonance. It proceeds to non-linear oscillators including non-linear resonance amplitude jumps and hysteresis. The non-linear restoring and friction forces also apply to electromechanical dynamos. These are examples of dynamical systems with bifurcations that may lead to chaotic motions. Presents general first-order differential equations including non-linear like the Ricatti equationDiscusses differentials of the first or higher order in two or more variablesIncludes discretization of differential equations as finite difference equationsDescribes parametric resonance of linear time dependent oscillators specified by the Mathieu functions and other methodsExamines non-linear oscillations and damping of dynamical systems including bifurcations and chaotic motions

GBP 44.99
1

An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models Fourth Edition provides a cohesive framework for statistical modelling with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations strategies for model selection and a Postface on good statistical practice. Like its predecessor this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal Poisson and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation the authors explore multiple linear regression analysis of variance (ANOVA) logistic regression log-linear models survival analysis multilevel modeling Bayesian models and Markov chain Monte Carlo (MCMC) methods. Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them Discusses common concepts and principles of advanced GLMs including nominal and ordinal regression survival analysis non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC methods to fit GLMs Contains numerous examples from business medicine engineering and the social sciences Provides the example code for R Stata and WinBUGS to encourage implementation of the methods Offers the data sets and solutions to the exercises online Describes the components of good statistical practice to improve scientific validity and reproducibility of results. Using popular statistical software programs this concise and accessible text illustrates practical approaches to estimation model fitting and model comparisons.

GBP 69.99
1

Bounds for Determinants of Linear Operators and their Applications

Bounds for Determinants of Linear Operators and their Applications

This book deals with the determinants of linear operators in Euclidean Hilbert and Banach spaces. Determinants of operators give us an important tool for solving linear equations and invertibility conditions for linear operators enable us to describe the spectra to evaluate the multiplicities of eigenvalues etc. We derive upper and lower bounds and perturbation results for determinants and discuss applications of our theoretical results to spectrum perturbations matrix equations two parameter eigenvalue problems as well as to differential difference and functional-differential equations. | Bounds for Determinants of Linear Operators and their Applications

GBP 150.00
1

Linear Synchronous Motors Transportation and Automation Systems Second Edition

Linear Synchronous Motors Transportation and Automation Systems Second Edition

Considered to be the first book devoted to the subject Linear Synchronous Motors: Transportation and Automation Systems Second Edition evaluates the state of the art demonstrating the technological innovations that are improving the design construction and performance of modern control systems. This new edition not only illustrates the development of linear synchronous motor drives but it also discusses useful techniques for selecting a motor that will meet the specific requirements of linear electrical drives. New Features for the Second Edition: Several updated and expanded sections as well as two new chapters on FEM Even more numerical examples calculations and mathematical models Broadened target audience that includes researchers scientists students and more Evaluating trends and practical techniques for achieving optimal system performance the authors showcase ready-to-implement solutions for common roadblocks in this process. The book presents fundamental equations and calculations used to determine and evaluate system operation efficiency and reliability with an exploration of modern computer-aided design of linear synchronous motors including the finite element approach. It covers topics such as linear sensors and stepping motors magnetic levitation systems elevators and factory automation systems. It also features case studies on flat PM tubular PM air-cored and hybrid linear synchronous motors as well as 3D finite element method analysis of tubular linear reluctance motors and linear oscillatory actuators. With such an exceptional presentation of practical tools and conceptual illustrations this volume is an especially powerful resource. It will benefit readers from all walks by providing numerical examples models guidelines and diagrams to help develop a clear understanding of linear synchronous motor operations characteristics and much more. | Linear Synchronous Motors Transportation and Automation Systems Second Edition

GBP 69.99
1

A Modern Introduction to Linear Algebra

A Modern Introduction to Linear Algebra

Useful Concepts and Results at the Heart of Linear AlgebraA one- or two-semester course for a wide variety of students at the sophomore/junior undergraduate levelA Modern Introduction to Linear Algebra provides a rigorous yet accessible matrix-oriented introduction to the essential concepts of linear algebra. Concrete easy-to-understand examples motivate the theory. The book first discusses vectors Gaussian elimination and reduced row echelon forms. It then offers a thorough introduction to matrix algebra including defining the determinant naturally from the PA=LU factorization of a matrix. The author goes on to cover finite-dimensional real vector spaces infinite-dimensional spaces linear transformations and complex vector spaces. The final chapter presents Hermitian and normal matrices as well as quadratic forms. Taking a computational algebraic and geometric approach to the subject this book provides the foundation for later courses in higher mathematics. It also shows how linear algebra can be used in various areas of application. Although written in a pencil and paper manner the text offers ample opportunities to enhance learning with calculators or computer usage. Solutions manual available for qualifying instructors

GBP 59.99
1

Exploring Linear Algebra Labs and Projects with MATLAB

Exploring Linear Algebra Labs and Projects with MATLAB

Exploring Linear Algebra: Labs and Projects with MATLAB® 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 algebra. For the most part labs discussed in the book can be used individually or in a sequence. Each lab consists of an explanation of material with integrated exercises. Some labs are split into multiple subsections and thus exercises are separated by those subsections. The exercise sections integrate problems using Mathematica demonstrations (an online tool that can be used with a browser with Java capabilities) and MATLAB® coding. This allows students to discover the theory and applications of linear algebra in a meaningful and memorable way. Features: The book’s inquiry-based approach promotes student interaction Each chapter contains a project set which consists of application-driven projects emphasizing the chapter’s materials Adds a project component to any Linear Algebra course Explores many applications to a variety of fields that can promote research projects Employs MATLAB® to calculate and explore concepts and theories of linear algebra | Exploring Linear Algebra Labs and Projects with MATLAB®

GBP 44.99
1

Computational Linear Algebra with Applications and MATLAB Computations

Computational Linear Algebra with Applications and MATLAB Computations

Courses on linear algebra and numerical analysis need each other. Often NA courses have some linear algebra topics and LA courses mention some topics from numerical analysis/scientific computing. This text merges these two areas into one introductory undergraduate course. It assumes students have had multivariable calculus. A second goal of this text is to demonstrate the intimate relationship of linear algebra to applications/computations. A rigorous presentation has been maintained. A third reason for writing this text is to present in the first half of the course the very important topic on singular value decomposition SVD. This is done by first restricting consideration to real matrices and vector spaces. The general inner product vector spaces are considered starting in the middle of the text. The text has a number of applications. These are to motivate the student to study the linear algebra topics. Also the text has a number of computations. MATLAB® is used but one could modify these codes to other programming languages. These are either to simplify some linear algebra computation or to model a particular application. | Computational Linear Algebra with Applications and MATLAB® Computations

GBP 44.99
1

Linear Algebra

Linear Algebra

Linear Algebra is intended primarily as an undergraduate textbook but is written in such a way that it can also be a valuable resource for independent learning. The narrative of the book takes a matrix approach: the exposition is intertwined with matrices either as the main subject or as tools to explore the theory. Each chapter contains a description of its aims a summary at the end of the chapter exercises and solutions. The reader is carefully guided through the theory and techniques presented which are outlined throughout in How to… text boxes. Common mistakes and pitfalls are also pointed out as one goes along. Features Written to be self-contained Ideal as a primary textbook for an undergraduate course in linear algebra Applications of the general theory which are of interest to disciplines outside of mathematics such as engineering

GBP 52.99
1

Control of Linear Systems

Control of Linear Systems

The book begins with the definition of time together with a brief description of its crucial properties and with the principles of the physical uniqueness and continuity of physical variables which are essential for the natural tracking control synthesis. The book presents further new results on matrices on polynomial matrices on matrix polynomials and on the new compact simple and elegant calculus that enabled the generalization of the transfer function matrix concept and of the state concept the proofs of the new observability controllability trackability and tracking conditions as well as the natural tracking control synthesis for all five classes of the treated linear systems. | Control of Linear Systems

GBP 130.00
1

Linear Electronics

Linear Electronics

A considerable amount of effort has been devoted both in industry and academia towards the design performance analysis and evaluation of amplification schemes and filters to be used in control systems audio/video equipment instrumentation and communication systems. This book is intended to serve as a complementary textbook for courses dealing with Linear Amplification but also as a professional book for engineers who need to update their knowledge in the electronics control and communications areas. The book is suitable for the undergraduate as well as the initial graduate levels of Electrical Engineering courses and is useful for the professional who wants to review or get acquainted with amplification theory. The book presents essential concepts in plain language and covers the most important applications of amplifier circuits. The book has four appendices an appendix to detail the operational amplifier model an appendix with specification data sheets an appendix on Fourier transform and signal spectrum including the concepts of convolution autocorrelation and power spectral density for deterministic signals and a final one that presents and explains the usual electronics acronyms. | Linear Electronics

GBP 94.99
1

Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

Helps Students Understand Mathematical Programming Principles and Solve Real-World ApplicationsSupplies enough mathematical rigor yet accessible enough for undergraduatesIntegrating a hands-on learning approach a strong linear algebra focus Maple™ software and real-world applications Linear and Nonlinear Programming with Maple™: An Interactive Applications-Based Approach introduces undergraduate students to the mathematical concepts and principles underlying linear and nonlinear programming. This text fills the gap between management science books lacking mathematical detail and rigor and graduate-level books on mathematical programming. Essential linear algebra toolsThroughout the text topics from a first linear algebra course such as the invertible matrix theorem linear independence transpose properties and eigenvalues play a prominent role in the discussion. The book emphasizes partitioned matrices and uses them to describe the simplex algorithm in terms of matrix multiplication. This perspective leads to streamlined approaches for constructing the revised simplex method developing duality theory and approaching the process of sensitivity analysis. The book also discusses some intermediate linear algebra topics including the spectral theorem and matrix norms. Maple enhances conceptual understanding and helps tackle problemsAssuming no prior experience with Maple the author provides a sufficient amount of instruction for students unfamiliar with the software. He also includes a summary of Maple commands as well as Maple worksheets in the text and online. By using Maple’s symbolic computing components numeric capabilities graphical versatility and intuitive programming structures students will acquire a deep conceptual understanding of major mathematical programming principles along with the ability to solve moderately sized rea | Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

GBP 59.99
1

Linear Optimization for Business Theory and practical application

Linear Optimization for Business Theory and practical application

This book takes a unique approach to linear optimization by focusing on the underlying principles and business applications of a topic more often taught from a mathematical and computational perspective. By shifting the perspective away from heavy math students learn how optimization can be used to drive decision making in real world business settings. The book does not shy away from the theory underlying linear optimization but rather focuses on ensuring students understand the logic without getting caught up in proving theorems. Plenty of examples applications and case studies are included to help bridge the gap between the theory and the way it plays out in practice. The author has also included several Excel spreadsheets showing worked-out models of linear optimization that have been used to drive decisions ranging from configuring a police force to purchasing crude oil and media planning. How can the routes and pricing structures of airlines be optimized? How much should be invested in the prevention and punishment of crimes? These are everyday problems that can be solved using linear optimization and this book shows students just how to do that. It will prove a useful math-free resource for all students of management science and operations research. | Linear Optimization for Business Theory and practical application

GBP 56.99
1

An R Companion to Linear Statistical Models

An R Companion to Linear Statistical Models

Focusing on user-developed programming An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

GBP 74.99
1

Linear Algebra and Its Applications with R

Linear Algebra and Its Applications with R

This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems providing the tools needed for real-world applications. Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. These exercises start from easy computations such as computing determinants of matrices to practical applications on simulated and empirical data sets with R so that students learn how to get started with R along with computational examples in each section and then students learn how to apply what they've learned to problems in applied sciences. This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and Python including tutorials on how to install R in the Appendix. If a student has never seen R they can get started without any additional help. Since Python is one of the most popular languages in data science optimization and computer science code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop students’ practical computational skills. About the Author: Dr. Ruriko Yoshida is an Associate Professor of Operations Research at the Naval Postgraduate School. She received her PhD in Mathematics from the University of California Davis. Her research topics cover a wide variety of areas: applications of algebraic combinatorics to statistical problems such as statistical learning on non-Euclidean spaces sensor networks phylogenetics and phylogenomics. She teaches courses in statistics stochastic models probability and data science. | Linear Algebra and Its Applications with R

GBP 82.99
1

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Reviewing the theory of the general linear model (GLM) using a general framework Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition presents analyses of simple and complex models both univariate and multivariate that employ data sets from a variety of disciplines such as the social and behavioral sciences. With revised examples that include options available using SAS 9. 0 this expanded edition divides theory from applications within each chapter. Following an overview of the GLM the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis and multivariate GLMs that cover multivariate regression analysis MANOVA MANCOVA and repeated measurement data analyses. The book also analyzes double multivariate linear growth curve seeming unrelated regression (SUR) restricted GMANOVA and hierarchical linear models. New to the Second EditionTwo chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedureExpanded theory of unrestricted general linear multivariate general linear SUR and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithmApplications of MI MIANALYZE TRANSREG and CALIS proceduresA practical introduction to GLMs Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework. | Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

GBP 56.99
1

Design of Experiments An Introduction Based on Linear Models

Design of Experiments An Introduction Based on Linear Models

Offering deep insight into the connections between design choice and the resulting statistical analysis Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models rather than as a collection of seemingly unrelated solutions to unique problems. The core material can be found in the first thirteen chapters. These chapters cover a review of linear statistical models completely randomized designs randomized complete blocks designs Latin squares analysis of data from orthogonally blocked designs balanced incomplete block designs random block effects split-plot designs and two-level factorial experiments. The remainder of the text discusses factorial group screening experiments regression model design and an introduction to optimal design. To emphasize the practical value of design most chapters contain a short example of a real-world experiment. Details of the calculations performed using R along with an overview of the R commands are provided in an appendix. This text enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an experiment. | Design of Experiments An Introduction Based on Linear Models

GBP 74.99
1