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Grundzüge der Globalen Optimierung
by Oliver SteinDas vorliegende Lehrbuch ist eine Einführung in die globale Optimierung, die mathematische Sachverhalte einerseits stringent behandelt, sie aber andererseits auch sehr ausführlich motiviert und mit 85 Abbildungen illustriert. Das Buch richtet sich daher nicht nur an Mathematiker, sondern auch an Natur-, Ingenieur- und Wirtschaftswissenschaftler, die mathematisch fundierte Verfahren in ihrem Gebiet verstehen und anwenden möchten. Mit fast zweihundert Seiten stellt das Buch genügend Auswahlmöglichkeiten zur Verfügung, um es als Grundlage für unterschiedlich angelegte Vorlesungen zur globalen Optimierung zu verwenden. Die ausführliche Behandlung der globalen Lösbarkeit von Optimierungsproblemen unter anwendungsrelevanten Voraussetzungen setzt dabei einen neuen Akzent, der den Bestand der bisherigen Lehrbücher zur Optimierung bereichert. Anhand von Theorie und Algorithmen der glatten konvexen Optimierung verdeutlicht das Buch, dass die globale Lösung einer in der Praxis häufig auftretenden Klasse von Optimierungsproblemen effizient möglich ist, während es für die schwerer handhabbaren nichtkonvexen Probleme ausführlich die Ideen von Branch-and-Bound-Verfahren entwickelt.Die vorliegende zweite Auflage wurde überarbeitet und um einige Passagen ergänzt.
Grundzüge der Konvexen Analysis
by Oliver SteinDieses Lehrbuch gibt eine verständliche Einführung in die konvexe Analysis, die mathematische Sachverhalte einerseits stringent behandelt, sie aber andererseits auch sehr ausführlich motiviert und mit vielen Abbildungen illustriert. Die Resultate werden anhand der geometrisch leicht nachvollziehbaren Fragestellung entwickelt, wie sich Hindernismengen und Verbotszonen mit garantierten Sicherheitsabständen modellieren lassen. Der Stoffaufbau mittels dieses durchgängigen Beispiels setzt einen neuen Akzent, der den Bestand der bisherigen Lehrbücher zur konvexen Analysis bereichert. Die erzielten Ergebnisse werden zudem auf nichtglatte konvexe Optimierungsprobleme angewendet, die in den Ingenieur- und Wirtschaftswissenschaften eine wichtige Rolle spielen. Das Buch richtet sich daher nicht nur an Mathematiker, sondern auch an Natur-, Ingenieur- und Wirtschaftswissenschaftler, die mathematisch fundierte Verfahren in ihrem Gebiet verstehen und anwenden möchten. Für Dozenten stellt das Buch genügend Auswahlmöglichkeiten zur Verfügung, um es als Grundlage für unterschiedlich angelegte Vorlesungen zur konvexen Analysis zu verwenden.
Grundzüge der Nichtlinearen Optimierung
by Oliver SteinDas vorliegende Lehrbuch ist eine Einführung in die nichtlineare Optimierung, die mathematische Sachverhalte einerseits stringent behandelt, sie aber andererseits auch sehr ausführlich motiviert und mit 39 Abbildungen illustriert. Das Buch richtet sich daher nicht nur an Mathematiker, sondern auch an Natur-, Ingenieur- und Wirtschaftswissenschaftler, die mathematisch fundierte Verfahren in ihrem Gebiet verstehen und anwenden möchten. Mit fast zweihundert Seiten stellt das Buch genügend Auswahlmöglichkeiten zur Verfügung, um es als Grundlage für unterschiedlich angelegte Vorlesungen zur nichtlinearen Optimierung zu verwenden. Viele geometrische Ansätze für das Verständnis sowohl von Optimalitätsbedingungen als auch von numerischen Verfahren setzen dabei einen neuen Akzent, der den Bestand der bisherigen Lehrbücher zur Optimierung bereichert. Dies betrifft insbesondere die ausführliche Behandlung der Probleme, die durch verschiedene funktionale Beschreibungen derselben Geometrie der Menge zulässiger Punkte entstehen können, und die dadurch motivierte Einführung von Constraint Qualifications für die Herleitung ableitungsbasierter Optimalitätsbedingungen.
Grundzüge der Nichtlinearen Optimierung
by Oliver SteinDas vorliegende Lehrbuch ist eine Einführung in die nichtlineare Optimierung, die mathematische Sachverhalte einerseits stringent behandelt, sie aber andererseits auch sehr ausführlich motiviert und mit 42 Abbildungen illustriert. Das Buch richtet sich daher nicht nur an Mathematiker, sondern auch an Natur-, Ingenieur- und Wirtschaftswissenschaftler, die mathematisch fundierte Verfahren in ihrem Gebiet verstehen und anwenden möchten. Mit etwas mehr als zweihundert Seiten stellt das Buch genügend Auswahlmöglichkeiten zur Verfügung, um es als Grundlage für unterschiedlich angelegte Vorlesungen zur nichtlinearen Optimierung zu verwenden. Viele geometrische Ansätze für das Verständnis sowohl von Optimalitätsbedingungen als auch von numerischen Verfahren setzen dabei einen neuen Akzent, der den Bestand der bisherigen Lehrbücher zur Optimierung bereichert. Dies betrifft insbesondere die ausführliche Behandlung der Probleme, die durch verschiedene funktionale Beschreibungen derselben Geometrie der Menge zulässiger Punkte entstehen können, und die dadurch motivierte Einführung von Constraint Qualifications für die Herleitung ableitungsbasierter Optimalitätsbedingungen. Die vorliegende zweite Auflage wurde überarbeitet und um einige Passagen ergänzt.
Grundzüge der Parametrischen Optimierung
by Oliver SteinDieses Lehrbuch gibt eine verständliche Einführung in die parametrische Optimierung, die mathematische Sachverhalte einerseits stringent behandelt, sie aber andererseits auch sehr ausführlich motiviert und mit vielen Abbildungen illustriert. Die vorwiegend geometrische Herleitung von zentralen Stabilitätsresultaten setzt dabei einen neuen Akzent, der den Bestand der bisherigen Lehrbücher zur parametrischen Optimierung bereichert. Die Stabilitäts- und Sensitivitätsergebnisse werden nicht nur mit speziellen ökonomischen Fragestellungen illustriert, sondern auch auf größere Problemklassen wie Nash-Spiele und die semi-infinite Optimierung angewendet, die in den Ingenieur- und Wirtschaftswissenschaften wichtige eine Rolle spielen. Das Buch richtet sich daher nicht nur an Mathematiker, sondern auch an Natur-, Ingenieur- und Wirtschaftswissenschaftler, die mathematisch fundierte Verfahren in ihrem Gebiet verstehen und anwenden möchten. Für Dozenten stellt das Buch genügend Auswahlmöglichkeiten zur Verfügung, um es als Grundlage für unterschiedlich angelegte Vorlesungen zur parametrischen Optimierung zu verwenden.
Guaranteed Computational Methods for Self-Adjoint Differential Eigenvalue Problems (SpringerBriefs in Mathematics)
by Xuefeng LiuThis monograph presents a study of newly developed guaranteed computational methodologies for eigenvalue problems of self-adjoint differential operators. It focuses on deriving explicit lower and upper bounds for eigenvalues, as well as explicit estimations for eigenfunction approximations. Such explicit error estimations rely on the finite element method (FEM) along with a new theory of explicit quantitative error estimation, diverging from traditional studies that primarily focus on qualitative results. To achieve quantitative error estimation, the monograph begins with an extensive analysis of the hypercircle method, that is, the Prager–Synge theorem. It introduces a novel a priori error estimation technique based on the hypercircle method. This facilitates the explicit estimation of Galerkin projection errors for equations such as Poisson's and Stokes', which are crucial for obtaining lower eigenvalue bounds via conforming FEMs. A thorough exploration of the fundamental theory of projection-based explicit lower eigenvalue bounds under a general setting of eigenvalue problems is also offered. This theory is extensively detailed when applied to model eigenvalue problems associated with the Laplace, biharmonic, Stokes, and Steklov differential operators, which are solved by either conforming or non-conforming FEMs. Moreover, there is a detailed discussion on the Lehmann–Goerisch theorem for the purpose of high-precision eigenvalue bounds, showing its relationship with previously established theorems, such as Lehmann–Maehly's method and Kato's bound. The implementation details of this theorem with FEMs, a topic rarely covered in existing literature, are also clarified. Lastly, the monograph introduces three new algorithms to estimate eigenfunction approximation errors, revealing the potency of classical theorems. Algorithm I extends Birkhoff’s result that works for simple eigenvalues to handle clustered eigenvalues, while Algorithm II generalizes the Davis–Kahan theorem, initially designed for strongly formulated eigenvalue problems, to address weakly formulated eigenvalue problems. Algorithm III utilizes the explicit Galerkin projection error estimation to efficiently handle Galerkin projection-based approximations.
Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
by Lawrence Weinstein John AdamGuesstimation is a book that unlocks the power of approximation--it's popular mathematics rounded to the nearest power of ten! The ability to estimate is an important skill in daily life. More and more leading businesses today use estimation questions in interviews to test applicants' abilities to think on their feet. Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic. Lawrence Weinstein and John Adam present an eclectic array of estimation problems that range from devilishly simple to quite sophisticated and from serious real-world concerns to downright silly ones. How long would it take a running faucet to fill the inverted dome of the Capitol? What is the total length of all the pickles consumed in the US in one year? What are the relative merits of internal-combustion and electric cars, of coal and nuclear energy? The problems are marvelously diverse, yet the skills to solve them are the same. The authors show how easy it is to derive useful ballpark estimates by breaking complex problems into simpler, more manageable ones--and how there can be many paths to the right answer. The book is written in a question-and-answer format with lots of hints along the way. It includes a handy appendix summarizing the few formulas and basic science concepts needed, and its small size and French-fold design make it conveniently portable. Illustrated with humorous pen-and-ink sketches, Guesstimation will delight popular-math enthusiasts and is ideal for the classroom.
Guesstimation 2.0: Solving Today's Problems on the Back of a Napkin
by Lawrence WeinsteinSimple and effective techniques for quickly estimating virtually anythingGuesstimation 2.0 reveals the simple and effective techniques needed to estimate virtually anything—quickly—and illustrates them using an eclectic array of problems. A stimulating follow-up to Guesstimation, this is the must-have book for anyone preparing for a job interview in technology or finance, where more and more leading businesses test applicants using estimation questions just like these.The ability to guesstimate on your feet is an essential skill to have in today's world, whether you're trying to distinguish between a billion-dollar subsidy and a trillion-dollar stimulus, a megawatt wind turbine and a gigawatt nuclear plant, or parts-per-million and parts-per-billion contaminants. Lawrence Weinstein begins with a concise tutorial on how to solve these kinds of order of magnitude problems, and then invites readers to have a go themselves. The book features dozens of problems along with helpful hints and easy-to-understand solutions. It also includes appendixes containing useful formulas and more.Guesstimation 2.0 shows how to estimate everything from how closely you can orbit a neutron star without being pulled apart by gravity, to the fuel used to transport your food from the farm to the store, to the total length of all toilet paper used in the United States. It also enables readers to answer, once and for all, the most asked environmental question of our day: paper or plastic?
A Guide for Statistical Tests and Interpretations with SPSS
by Arshia U. ZaidiA Guide for Statistical Tests and Interpretations with SPSS is designed for students taking basic and advanced courses in statistics, taking an integrative and practical approach to learning statistics. It guides students through navigating SPSS outputs and writing quantitatively, dealing with technical and substantive interpretations without resorting to complex mathematical formulae.Starting from the basics of quantitative research methods and discussing descriptive and inferential statistical tests, this book provides a unique perspective of data analysis with SPSS. It makes a conscious effort to explore the various statistical methods one can use to dissect a data set using basic or advanced statistical techniques to achieve the best outcome. It covers the practical questions that arise while doing an assignment, final paper, or thesis – showing students how to proceed to the next step in their interpretation and analysis. It will provide quantitative methodology or data analysis students with core interpretations of SPSS outputs for key statistical tests. It will also demonstrate how to select and report the key trends and patterns of the data using descriptive and inferential statistics, the requirements and/or assumptions of each test, as well as the precise language to use for reporting on each test.With SPSS screenshots and step-by-step advice, this book will be useful for all undergraduate and graduate students in the social sciences and humanities, as a supplemental textbook to provide practical guidance on moving through all steps of statistical testing and analysis.
A Guide for the Statistically Perplexed
by David L. Streiner Canadian Psychiatric AssociationDo statistics-heavy research papers give you a headache? Are you baffled by bias, confused by correlation, or flummoxed by F-tests? A Guide for the Statistically Perplexed is here to help! This book is designed to assist students, clinicians, and researchers in becoming familiar with statistical and research techniques by covering the essentials of the topic and drawing attention to many common problem areas.Inspired to write on this topic in reaction to mistakes he encountered in actual papers, David L. Streiner uses his trademark sense of humour and light-hearted style to explain complex statistical concepts in lucid, jargon-free language. Streiner delves into topics such as presenting data (or, conversely, how not to), statistical techniques, and more advanced procedures. To help readers detect problems with research design and interpretation, he details important 'CRAP' (convoluted reasoning or anti-intellectual pomposity) detectors for which they should watch out.Even those with little or no background in statistics, measurement theory, or research will come out of A Guide for the Statistically Perplexed with a new understanding and appreciation of these topics.
Guide to 3D Vision Computation
by Kenichi Kanatani Yasuyuki Sugaya Yasushi KanazawaThis classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.
A Guide to Algorithm Design: Paradigms, Methods, and Complexity Analysis (Chapman & Hall/CRC Applied Algorithms and Data Structures series)
by Anne Benoit Yves Robert Frédéric VivienPresenting a complementary perspective to standard books on algorithms, A Guide to Algorithm Design: Paradigms, Methods, and Complexity Analysis provides a roadmap for readers to determine the difficulty of an algorithmic problem by finding an optimal solution or proving complexity results. It gives a practical treatment of algorithmic complexity and guides readers in solving algorithmic problems.Divided into three parts, the book offers a comprehensive set of problems with solutions as well as in-depth case studies that demonstrate how to assess the complexity of a new problem. Part I helps readers understand the main design principles and design efficient algorithms. Part II covers polynomial reductions from NP-complete problems and approaches that go beyond NP-completeness. Part III supplies readers with tools and techniques to evaluate problem complexity, including how to determine which instances are polynomial and which are NP-hard. Drawing on the authors’ classroom-tested material, this text takes readers step by step through the concepts and methods for analyzing algorithmic complexity. Through many problems and detailed examples, readers can investigate polynomial-time algorithms and NP-completeness and beyond.
A Guide to Business Mathematics
by Gerard O'ReganThe success of business today is dependent on the knowledge and expertise of its employees. The need for mathematics arises naturally in business such as in the work of the actuary in an insurance company, the financial mathematics required in the day-to-day work of the banker and the need to analyse data to extract useful information to enable the business to make the right decisions to be successful. A Guide to Business Mathematics provides a valuable self-study guide to business practitioners, business students and the general reader to enable them to gain an appropriate insight into the mathematics used in business. This book offers an accessible introduction to essential mathematics for the business field. A wide selection of topics is discussed with the mathematical material presented in a reader-friendly way. The business context motivates the presentation. The author uses modelling and applications to motivate the material, demonstrating how mathematics is used in the financial sector. In addition to the role of the actuary and the banker, the book covers operations research including game theory, trade discounts and the fundamentals of statistics and probability. The book is also a guide to using metrics to manage and measure performance, and business economics. Foundations on algebra, number theory, sequences and series, matrix theory and calculus are included as is a complete chapter on using software. Features • Discusses simple interest and its application to promissory notes/treasury bills. • Discusses compound interest with applications to present and future values. • Introduces the banking field including loans, annuities and the spot/forward FX market. • Discusses trade discounts and markups/markdowns. • Introduces the insurance field and the role of the actuary. • Introduces the fields of data analytics and operations research. • Discusses business metrics and problem solving. • Introduces matrices and their applications. • Discusses calculus and its applications. • Discusses basic financial statements such as balance sheet, profit and loss and cash account. • Reviews a selection of software to support business mathematics. This broad-ranging text gives the reader a flavour of the applications of mathematics to the business field and stimulates further study in the subject. As such, it will be of great benefit to business students, while also capturing the interest of the more casual reader. About the Author Dr. Gerard O'Regan is an Assistant Professor in Mathematics at the University of Central Asia in Kyrgyzstan. His research interests include software quality and software process improvement, mathematical approaches to software quality, and the history of computing. He is the author of several books in the Mathematics and Computing fields.
A Guide to Business Statistics
by David M. McEvoyAn accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more streamlined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are introduced through examples, and the text is designed to be accessible to readers with a variety of backgrounds. To enhance learning, most of the mathematical formulae and notation appears in technical appendices at the end of each chapter. This important resource: • Offers a comprehensive guide to understanding business statistics targeting business and economics students and professionals • Introduces the concepts and techniques through concise and intuitive examples • Focuses on understanding by moving distracting formulae and mathematical notation to appendices • Offers intuition, insights, humor, and practical advice for students of business statistics • Features coverage of sampling techniques, descriptive statistics, probability, sampling distributions, confidence intervals, hypothesis tests, and regression Written for undergraduate business students, business and economics majors, teachers, and practitioners, A Guide to Business Statistics offers an accessible guide to the key concepts and fundamental principles in statistics.DAVID M. McEVOY, PhD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years. DAVID M. McEVOY, PhD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more streamlined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are introduced through examples, and the text is designed to be accessible
Guide to Classical Physics: Using Mathematica for Calculations and Visualizations
by James W. RohlfThis is a “how to guide” for making introductory calculations in classical physics for undergraduates studying the subject.The calculations are performed in Mathematica, and stress graphical visualization, units, and numerical answers. The techniques show the student how to learn the physics without being hung up on the math. There is a continuing movement to introduce more advanced computational methods into lower-level physics courses. Mathematica is a unique tool in that code is written as "human readable" much like one writes a traditional equation on the board.The companion code for this book can be found here: https://physics.bu.edu/~rohlf/code.htmlKey Features:• Concise summary of the physics concepts• Over 300 worked examples in Mathematica• Tutorial to allow a beginner to produce fast resultsThe companion code for this book can be found here: https://physics.bu.edu/~rohlf/code.html
Guide to Cloud Computing for Business and Technology Managers: From Distributed Computing to Cloudware Applications
by Vivek KaleGuide to Cloud Computing for Business and Technology Managers: From Distributed Computing to Cloudware Applications unravels the mystery of cloud computing and explains how it can transform the operating contexts of business enterprises. It provides a clear understanding of what cloud computing really means, what it can do, and when it is practical
A Guide to Detracking Math Courses: The Journey to Realize Equity and Access in K-12 Mathematics Education (Corwin Mathematics Series)
by Angela Nicole Torres Ho Hai Nguyen Elizabeth Crawford Hull Barnes Laura Wentworth StreeterCreate a pathway to equity by detracking mathematics The tracked mathematics system has been operating in US schools for decades. However, research demonstrates negative effects on subgroups of students by keeping them in a single math track, thereby denying them access to rigorous coursework needed for college and career readiness. The journey to change this involves confronting some long-standing beliefs and structures in education. When supported with the right structures, instructional shifts, coalition building, and educator training and support, the detracking of mathematics courses can be a primary pathway to equity. The ultimate goal is to increase more students’ access to and achievement in higher levels of mathematics learning–especially for students who are historically marginalized. Based on the stories and lessons learned from the San Francisco Unified School District educators who have talked the talk and walked the walk, this book provides a model for all those involved in taking on detracking efforts from policymakers and school administrators, to math coaches and teachers. By sharing stories of real-world examples, lessons learned, and prompts to provoke discussion about your own context, the book walks you through: Designing and gaining support for a policy of detracked math courses Implementing the policy through practical shifts in scheduling, curriculum, professional development, and coaching Supporting and improving the policy through continuous research, monitoring, and maintenance. This book offers the big ideas that help you in your own unique journey to advance equity in your school or district’s mathematics education and also provides practical information to help students in a detracked system thrive.
A Guide to Detracking Math Courses: The Journey to Realize Equity and Access in K-12 Mathematics Education (Corwin Mathematics Series)
by Angela Nicole Torres Ho Hai Nguyen Elizabeth Crawford Hull Barnes Laura Wentworth StreeterCreate a pathway to equity by detracking mathematics The tracked mathematics system has been operating in US schools for decades. However, research demonstrates negative effects on subgroups of students by keeping them in a single math track, thereby denying them access to rigorous coursework needed for college and career readiness. The journey to change this involves confronting some long-standing beliefs and structures in education. When supported with the right structures, instructional shifts, coalition building, and educator training and support, the detracking of mathematics courses can be a primary pathway to equity. The ultimate goal is to increase more students’ access to and achievement in higher levels of mathematics learning–especially for students who are historically marginalized. Based on the stories and lessons learned from the San Francisco Unified School District educators who have talked the talk and walked the walk, this book provides a model for all those involved in taking on detracking efforts from policymakers and school administrators, to math coaches and teachers. By sharing stories of real-world examples, lessons learned, and prompts to provoke discussion about your own context, the book walks you through: Designing and gaining support for a policy of detracked math courses Implementing the policy through practical shifts in scheduling, curriculum, professional development, and coaching Supporting and improving the policy through continuous research, monitoring, and maintenance. This book offers the big ideas that help you in your own unique journey to advance equity in your school or district’s mathematics education and also provides practical information to help students in a detracked system thrive.
Guide to Discrete Mathematics
by Gerard O'ReganThis stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics; examines the field of software engineering, describing formal methods; investigates probability and statistics.
Guide to Discrete Mathematics: An Accessible Introduction to the History, Theory, Logic and Applications (Texts in Computer Science)
by Gerard O'ReganThis stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: This book provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics such as the nature of theorem proving; examines the field of software engineering, including software reliability and dependability and describes formal methods; investigates probability and statistics and presents an overview of operations research and financial mathematics.
A Guide to Doing Statistics in Second Language Research Using SPSS
by Jenifer Larson-HallThis valuable book shows second language researchers how to use the statistical program SPSS to conduct statistical tests frequently done in SLA research. Using data sets from real SLA studies, A Guide to Doing Statistics in Second Language Research Using SPSS shows newcomers to both statistics and SPSS how to generate descriptive statistics, how to choose a statistical test, and how to conduct and interpret a variety of basic statistical tests. It covers the statistical tests that are most commonly used in second language research, including chi-square, t-tests, correlation, multiple regression, ANOVA and non-parametric analogs to these tests. The text is abundantly illustrated with graphs and tables depicting actual data sets, and exercises throughout the book help readers understand concepts (such as the difference between independent and dependent variables) and work out statistical analyses. Answers to all exercises are provided on the book’s companion website, along with sample data sets and other supplementary material.
A Guide to Doing Statistics in Second Language Research Using SPSS and R (Second Language Acquisition Research Series)
by Jenifer Larson-HallA Guide to Doing Statistics in Second Language Research Using SPSS and R, Second Edition is the only text available that demonstrates how to use SPSS and R as specifically related to applied linguistics and SLA research. This new edition is up-to-date with the most recent version of the SPSS software and now also includes coverage of R, a software program increasingly used by researchers in this field. Supported by a number of pedagogical features, including tip boxes and practice activities, and a wealth of screenshots, this book takes readers through each step of performing and understanding statistical research, covering the most commonly used tests in second language research, including t-tests, correlation, and ANOVA. A robust accompanying website covers additional tests of interest to students and researchers, taking them step-by-step through carrying out these tests themselves. In this comprehensive and hands-on volume, Jenifer Larson-Hall equips readers with a thorough understanding and the practical skills necessary to conducting and interpreting statisical research effectively using SPSS and R, ideal for graduate students and researchers in SLA, social sciences, and applied lingustics. For more information and materials, please visit www.routledge.com/cw/larson-hall.
A Guide to Econometrics (6th edition)
by Peter KennedyThis book designed to illuminate the logic of econometrics without formulas, providing intuition, skepticism, insights, humor, and practical advice. Designed for use in a range of courses, from undergraduate to graduate and PhD level.
A Guide to Empirical Orthogonal Functions for Climate Data Analysis
by Valeria Simoncini Antonio NavarraClimatology and meteorology have basically been a descriptive science until it became possible to use numerical models, but it is crucial to the success of the strategy that the model must be a good representation of the real climate system of the Earth. Models are required to reproduce not only the mean properties of climate, but also its variability and the strong spatial relations between climate variability in geographically diverse regions. Quantitative techniques were developed to explore the climate variability and its relations between different geographical locations. Methods were borrowed from descriptive statistics, where they were developed to analyze variance of related observations-variable pairs, or to identify unknown relations between variables. A Guide to Empirical Orthogonal Functions for Climate Data Analysis uses a different approach, trying to introduce the reader to a practical application of the methods, including data sets from climate simulations and MATLAB codes for the algorithms. All pictures and examples used in the book may be reproduced by using the data sets and the routines available in the book . Though the main thrust of the book is for climatological examples, the treatment is sufficiently general that the discussion is also useful for students and practitioners in other fields. Supplementary datasets are available via http://extra.springer.com
A Guide to Implementing MLOps: From Data to Operations (Synthesis Lectures on Engineering, Science, and Technology)
by Prafful MishraOver the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.