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Introduction to Mixed Modelling
by N. W. GalweyMixed modelling is very useful, and easier than you think!Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained.Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts.Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following:Use of the software SAS, in addition to GenStat and R.Meta-analysis and the multiple testing problem.The Bayesian interpretation of mixed models.Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography.
Introduction to Model Spaces and their Operators
by Javad Mashreghi Stephan Ramon Garcia William T. RossThe study of model spaces, the closed invariant subspaces of the backward shift operator, is a vast area of research with connections to complex analysis, operator theory and functional analysis. This self-contained text is the ideal introduction for newcomers to the field. It sets out the basic ideas and quickly takes the reader through the history of the subject before ending up at the frontier of mathematical analysis. Open questions point to potential areas of future research, offering plenty of inspiration to graduate students wishing to advance further.
Introduction to Model Theory (Algebra, Logic and Applications)
by Philipp RothmalerModel theory investigates mathematical structures by means of formal languages. So-called first-order languages have proved particularly useful in this respect.This text introduces the model theory of first-order logic, avoiding syntactical issues not too relevant to model theory. In this spirit, the compactness theorem is proved via the algebraically useful ultrsproduct technique (rather than via the completeness theorem of first-order logic). This leads fairly quickly to algebraic applications, like Malcev's local theorems of group theory and, after a little more preparation, to Hilbert's Nullstellensatz of field theory.Steinitz dimension theory for field extensions is obtained as a special case of a much more general model-theoretic treatment of strongly minimal theories. There is a final chapter on the models of the first-order theory of the integers as an abelian group. Both these topics appear here for the first time in a textbook at the introductory level, and are used to give hints to further reading and to recent developments in the field, such as stability (or classification) theory.
Introduction to Modeling Convection in Planets and Stars: Magnetic Field, Density Stratification, Rotation (Princeton Series in Astrophysics #24)
by Gary A. GlatzmaierThis book provides readers with the skills they need to write computer codes that simulate convection, internal gravity waves, and magnetic field generation in the interiors and atmospheres of rotating planets and stars. Using a teaching method perfected in the classroom, Gary Glatzmaier begins by offering a step-by-step guide on how to design codes for simulating nonlinear time-dependent thermal convection in a two-dimensional box using Fourier expansions in the horizontal direction and finite differences in the vertical direction. He then describes how to implement more efficient and accurate numerical methods and more realistic geometries in two and three dimensions. In the third part of the book, Glatzmaier demonstrates how to incorporate more sophisticated physics, including the effects of magnetic field, density stratification, and rotation. Featuring numerous exercises throughout, this is an ideal textbook for students and an essential resource for researchers. Describes how to create codes that simulate the internal dynamics of planets and stars Builds on basic concepts and simple methods Shows how to improve the efficiency and accuracy of the numerical methods Describes more relevant geometries and boundary conditions Demonstrates how to incorporate more sophisticated physics
Introduction to Modeling Sustainable Development in Business Processes: Theory and Case Studies
by Dirk InghelsSustainable development and corporate social responsibility drive countries, regions, and businesses to take environmental and social concerns into account when realizing economic objectives. A growing awareness of the connectedness between industrial, societal, and environmental systems might shift the way businesses will be operated. This book aims to help students and business practitioners use quantitative modeling in their pursuit to make business processes sustainable. Two approaches are introduced: linear optimization and system dynamics. Moreover, the quantification of the three different sustainability objectives is also addressed. Next to introducing the theoretical background, many real-life examples are discussed to demonstrate how the modelling techniques can be applied.
Introduction to Modeling and Numerical Methods for Biomedical and Chemical Engineers
by Edward GatzkeThis textbook introduces the concepts and tools that biomedical and chemical engineering students need to know in order to translate engineering problems into a numerical representation using scientific fundamentals. Modeling concepts focus on problems that are directly related to biomedical and chemical engineering. A variety of computational tools are presented, including MATLAB, Excel, Mathcad, and COMSOL, and a brief introduction to each tool is accompanied by multiple computer lab experiences. The numerical methods covered are basic linear algebra and basic statistics, and traditional methods like Newton’s method, Euler Integration, and trapezoidal integration. The book presents the reader with numerous examples and worked problems, and practice problems are included at the end of each chapter.
Introduction to Modeling and Simulation with MATLAB® and Python (Chapman & Hall/CRC Computational Science)
by Brian Guilfoos Steven I. GordonIntroduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.
Introduction to Modeling for Biosciences
by Dominique Chu David J. BarnesMathematical modeling can be a useful tool for researchers in the biological scientists. Yet in biological modeling there is no one modeling technique that is suitable for all problems. Instead, different problems call for different approaches. Furthermore, it can be helpful to analyze the same system using a variety of approaches, to be able to exploit the advantages and drawbacks of each. In practice, it is often unclear which modeling approaches will be most suitable for a particular biological question, a problem which requires researchers to know a reasonable amount about a number of techniques, rather than become experts on a single one. "Introduction to Modeling for Biosciences" addresses this issue by presenting a broad overview of the most important techniques used to model biological systems. In addition to providing an introduction into the use of a wide range of software tools and modeling environments, this helpful text/reference describes the constraints and difficulties that each modeling technique presents in practice, enabling the researcher to quickly determine which software package would be most useful for their particular problem. Topics and features: introduces a basic array of techniques to formulate models of biological systems, and to solve them; intersperses the text with exercises throughout the book; includes practical introductions to the Maxima computer algebra system, the PRISM model checker, and the Repast Simphony agent modeling environment; discusses agent-based models, stochastic modeling techniques, differential equations and Gillespie's stochastic simulation algorithm; contains appendices on Repast batch running, rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts; supplies source code for many of the example models discussed, at the associated website http://www.cs.kent.ac.uk/imb/. This unique and practical guide leads the novice modeler through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model. Students and active researchers in the biosciences will also benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book. Dr. David J. Barnes is a lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming. Dr. Dominique Chu is a lecturer in computer science at the University of Kent, UK. He is an internationally recognized expert in agent-based modeling, and has also in-depth research experience in stochastic and differential equation based modeling.
Introduction to Modern Algebra and Its Applications
by Nadiya GubareniThe book provides an introduction to modern abstract algebra and its applications. It covers all major topics of classical theory of numbers, groups, rings, fields and finite dimensional algebras. The book also provides interesting and important modern applications in such subjects as Cryptography, Coding Theory, Computer Science and Physics. In particular, it considers algorithm RSA, secret sharing algorithms, Diffie-Hellman Scheme and ElGamal cryptosystem based on discrete logarithm problem. It also presents Buchberger’s algorithm which is one of the important algorithms for constructing Gröbner basis. Key Features: Covers all major topics of classical theory of modern abstract algebra such as groups, rings and fields and their applications. In addition it provides the introduction to the number theory, theory of finite fields, finite dimensional algebras and their applications. Provides interesting and important modern applications in such subjects as Cryptography, Coding Theory, Computer Science and Physics. Presents numerous examples illustrating the theory and applications. It is also filled with a number of exercises of various difficulty. Describes in detail the construction of the Cayley-Dickson construction for finite dimensional algebras, in particular, algebras of quaternions and octonions and gives their applications in the number theory and computer graphics.
Introduction to Modern Algebra and Matrix Theory: Second Edition
by Martin David Melvin Hausner O. Schreier E. SpernerThis unique text provides students with a basic course in both calculus and analytic geometry -- no competitive editions cover both topics in a single volume. Its prerequisites are minimal, and the order of its presentation promotes an intuitive approach to calculus. Algebraic concepts receive an unusually strong emphasis. Numerous exercises appear throughout the text. 1951 edition.
Introduction to Modern Cryptography (Chapman & Hall/CRC Cryptography and Network Security Series)
by Jonathan Katz Yehuda LindellNow the most used texbook for introductory cryptography courses in both mathematics and computer science, the Third Edition builds upon previous editions by offering several new sections, topics, and exercises. The authors present the core principles of modern cryptography, with emphasis on formal definitions, rigorous proofs of security.
Introduction to Modern Cryptography: Fourth Edition (Chapman & Hall/CRC Cryptography and Network Security Series)
by Jonathan Katz Yehuda LindellIntroduction to Modern Cryptography, the most relied-upon textbook in the field, provides a mathematically rigorous yet accessible treatment of this fascinating subject. The authors have kept the book up-to-date while incorporating feedback from instructors and students alike; the presentation is refined, current, and accurate.The book’s focus is on modern cryptography, which is distinguished from classical cryptography by its emphasis on definitions, precise assumptions, and rigorous proofs of security. A unique feature of the text is that it presents theoretical foundations with an eye toward understanding cryptography as used in the real world. This revised edition fixed typos and includes all the updates made to the third edition, including: Enhanced treatment of several modern aspects of private-key cryptography, including authenticated encryption and nonce-based encryption. Coverage of widely used standards such as GMAC, Poly1305, GCM, CCM, and ChaCha20-Poly1305. New sections on the ChaCha20 stream cipher, sponge-based hash functions, and SHA-3. Increased coverage of elliptic-curve cryptography, including a discussion of various curves used in practice. A new chapter describing the impact of quantum computers on cryptography and providing examples of quantum-secure encryption and signature schemes. Containing worked examples and updated exercises, Introduction to Modern Cryptography, Revised Third Edition can serve as a textbook for undergraduate- or graduate-level courses in cryptography, a reference for graduate students, researchers, and practitioners, or a general introduction suitable for self-study.
Introduction to Modern Modelling Methods (The SAGE Quantitative Research Kit)
by D. Betsy McCoach Dakota CintronUsing simple and direct language, this concise text provides practical guidance on a wide range of modeling methods and techniques for use with quantitative data. It covers: · 2-level Multilevel Models · Structural Equation Modeling (SEM) · Longitudinal Modeling using multilevel and SEM techniques · Combining organizational and longitudinal models Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Introduction to Modern Modelling Methods (The SAGE Quantitative Research Kit)
by D. Betsy McCoach Dakota CintronUsing simple and direct language, this concise text provides practical guidance on a wide range of modeling methods and techniques for use with quantitative data. It covers: · 2-level Multilevel Models · Structural Equation Modeling (SEM) · Longitudinal Modeling using multilevel and SEM techniques · Combining organizational and longitudinal models Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Introduction to Modern Scientific Programming and Numerical Methods
by Joseph Wang Lubos BriedaThe ability to use computers to solve mathematical relationships is a fundamental skill for anyone planning for a career in science or engineering. For this reason, numerical analysis is part of the core curriculum for just about every undergraduate physics and engineering department. But for most physics and engineering students, practical programming is a self-taught process.This book introduces the reader not only to the mathematical foundation but also to the programming paradigms encountered in modern hybrid software-hardware scientific computing. After completing the text, the reader will be well-versed in the use of different numerical techniques, programming languages, and hardware architectures, and will be able to select the appropriate software and hardware tool for their analysis.It can serve as a textbook for undergraduate courses on numerical analysis and scientific computing courses within engineering and physical sciences departments. It will also be a valuable guidebook for researchers with experimental backgrounds interested in working with numerical simulations, or to any new personnel working in scientific computing or data analysis.Key Features: Includes examples of solving numerical problems in multiple programming languages, including MATLAB, Python, Fortran, C++, Arduino, Javascript, and Verilog Provides an introduction to modern high-performance computing technologies including multithreading, distributed computing, GPUs, microcontrollers, FPGAs, and web "cloud computing" Contains an overview of numerical techniques not found in other introductory texts including particle methods, finite volume and finite element methods, Vlasov solvers, and molecular dynamics
Introduction to Modern Statistics
by Mine Çetinkaya-Rundel Johanna HardinIntroduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches. Other highlights include: <p><p>Web native book. The online book is available in HTML, which offers easy navigation and searchability in the browser. The book is built with the bookdown package and the source code to reproduce the book can be found on GitHub. Along with the bookdown site, this book is also available as a PDF and in paperback. <p><p>Tutorials. While the main text of the book is agnostic to statistical software and computing language, each part features 4-8 interactive R tutorials (for a total of 32 tutorials) that walk you through the implementation of the part content in R with the tidyverse for data wrangling and visualisation and the tidyverse-friendly infer package for inference. The self-paced and interactive R tutorials were developed using the learnr R package, and only an internet browser is needed to complete them. <p><p>Labs. Each part also features 1-2 R based labs. The labs consist of data analysis case studies and they also make heavy use of the tidyverse and infer packages. <p><p>Datasets. Datasets used in the book are marked with a link to where you can find the raw data. The majority of these point to the openintro package. You can install the openintro package from CRAN or get the development version on GitHub.
Introduction to Multivariate Analysis (Chapman And Hall/crc Texts In Statistical Science Ser. #1)
by Chris ChatfieldThis book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.
Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science #134)
by Sadanori KonishiThis text shows how to use multivariate analysis to extract useful information from multivariate data and understand the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification, discrimination, dimension reduction, and clustering. Many examples and figures throughout facilitate a deep understanding of the multivariate analysis techniques, including how to select the optimal model.
Introduction to Multivariate Calibration: A Practical Approach
by Alejandro C. OlivieriThis book offers an introductory-level guide to the complex field of multivariate analytical calibration, with particular emphasis on real applications such as near infrared spectroscopy. It presents intuitive descriptions of mathematical and statistical concepts, illustrated with a wealth of figures and diagrams, and consistently highlights physicochemical interpretation rather than mathematical issues. In addition, it describes an easy-to-use and freely available graphical interface, together with a variety of appropriate examples and exercises. Lastly, it discusses recent advances in the field (figures of merit, detection limit, non-linear calibration, method comparison), together with modern literature references.
Introduction to Multivariate Calibration: A Practical Approach
by Alejandro C. OlivieriThis book contains several new sections that provide even more in-depth knowledge on the topics. New content on the classical least-squares model, which shows its advantages and limitations in greater detail, was added. Additionally, the book contains a new section on the inverse least-squares model, which explains how it differs from the classical model and its applications in chemometrics. Furthermore, a new chapter on principal component analysis, which covers the concept in greater detail and its applications in chemometrics, is added. This book also includes several real-world examples to help you better understand the topic. Overall, this book provides the reader with even more comprehensive knowledge on chemometrics and multivariate calibration, making it an essential resource for students and professionals alike.
Introduction to Multivariate Statistical Analysis in Chemometrics
by Peter Filzmoser Kurt VarmuzaUsing formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as
Introduction to NFL Analytics with R (Chapman & Hall/CRC Data Science Series)
by Bradley J. CongelioIt has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free manner.Key Coverage:• Installing R, RStudio, and necessary packages• Working and becoming fluent in the tidyverse• Finding meaning in NFL data with examples from all the functions in the nflverse family of packages• Using NFL data to create eye-catching data visualizations• Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient BoostingThe book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.
Introduction to Network Security (Chapman & Hall/CRC Cryptography and Network Security Series)
by Douglas JacobsonUnlike data communications of the past, today's networks consist of numerous devices that handle the data as it passes from the sender to the receiver. However, security concerns are frequently raised in circumstances where interconnected computers use a network not controlled by any one entity or organization. Introduction to Network Security exam
Introduction to Non-Euclidean Geometry (Dover Books on Mathematics)
by Harold E. WolfeOne of the first college-level texts for elementary courses in non-Euclidean geometry, this concise, readable volume is geared toward students familiar with calculus. A full treatment of the historical background explores the centuries-long efforts to prove Euclid's parallel postulate and their triumphant conclusion. Numerous original exercises form an integral part of the book.Topics include hyperbolic plane geometry and hyperbolic plane trigonometry, applications of calculus to the solutions of some problems in hyperbolic geometry, elliptic plane geometry and trigonometry, and the consistency of the non-Euclidean geometries. Extensive appendixes offer background information on the foundation of Euclidean geometry, circular and hyperbolic functions, the theory of orthogonal circles and allied topics, and the elements of inversion.
Introduction to Non-linear Mechanics: A Unified Energetical Approach (Springer Series in Solid and Structural Mechanics #14)
by Claude StolzThis book presents an introduction to the non-linear mechanics of materials, focusing on a unified energetical approach. It begins by summarizing the framework of a thermodynamic description of continua, including a description of the kinematics of deformation, and a summary of the equations of motion. After a short description of the motion of the system and the mechanical interaction, the book introduces the Lagrangean and Hamiltonian functionals of the system, transitioning to the quasistatic characterization with emphasis on the role of potential energy and pseudo-potential of dissipation. The framework is then extended to fracture and damage mechanics with a similar energetical approach proposed for material damage and wear. The book looks at homogenization in non-linear mechanics for locally plastic or damaged material with an analysis of stability and bifurcation of the equilibrium path. Lastly, inverse problems in non-linear mechanics are introduced using optimal control theory. All the concepts introduced in the book are illustrated using analytical solutions on beams, rods, plates, or using spherical and cylindrical symmetries. Graduate students and researchers working on continuum mechanics and interested in a deeper understanding of materials damage, wear, and fatigue will find this book instructive and informative.