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Applying Fuzzy Logic for the Digital Economy and Society (Fuzzy Management Methods)
by Andreas Meier Edy Portmann Luis TeránThis edited book presents the state-of-the-art of applying fuzzy logic to managerial decision-making processes in areas such as fuzzy-based portfolio management, recommender systems, performance assessment and risk analysis, among others. Presenting the latest research, with a strong focus on applications and case studies, it is a valuable resource for researchers, practitioners, project leaders and managers wanting to apply or improve their fuzzy-based skills.
Applying Graph Theory in Ecological Research
by Dale Mark R. T.Graph theory can be applied to ecological questions in many ways, and more insights can be gained by expanding the range of graph theoretical concepts applied to a specific system. But how do you know which methods might be used? And what do you do with the graph once it has been obtained? This book provides a broad introduction to the application of graph theory in different ecological systems, providing practical guidance for researchers in ecology and related fields. Readers are guided through the creation of an appropriate graph for the system being studied, including the application of spatial, spatio-temporal, and more abstract structural process graphs. Simple figures accompany the explanations to add clarity, and a broad range of ecological phenomena from many ecological systems are covered. This is the ideal book for graduate students and researchers looking to apply graph theoretical methods in their work.
Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries
by Sam MorleyThis book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems, 2nd Edition
by Sam MorleyDiscover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to use Python libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionThe updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnBecome familiar with basic Python packages, tools, and libraries for solving mathematical problemsExplore real-world applications of mathematics to reduce a problem in optimizationUnderstand the core concepts of applied mathematics and their application in computer scienceFind out how to choose the most suitable package, tool, or technique to solve a problemImplement basic mathematical plotting, change plot styles, and add labels to plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forWhether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
Applying Mathematics: Tests (Mathematics For Christian Living)
by Glenn Auker Seth Rudolph Keith KriederApplying Mathematics Grade 8 Math Chapter Tests
Applying Mathematics: Grade 8 (Mathematics For Christian Living Series)
by John Mark Shenk Marian Baltozer Amy Herr Christine Collins Glenn Auker Seth Rudolph Keith KriederThe teacher's manual comes in two volumes. Each lesson has full-size pupil's pages, with answers filled in. Extra pages guide the teacher in lesson preparation and include answer keys for quizzes, speed teats, and chapter tests.
Applying Maths in Construction
by Antoinette Tourret John HumphreysThis book and its accompanying Teacher's Pack are the result of a project, supported by the Nuffield Foundation, to provide flexible learning materials for the Basic Application of Number core skill for both the NVQs and GNVQ in construction and the construction crafts. The student book uses a unique approach to explain how mathematical principles apply to construction tasks. Each chapter forms an individual construction project and uses the full range of number skills from the fundamentals of addition and subtraction to statistics, trigonometry and technical drawing. Successfully completed projects provide the student with the required portfolio of evidence for their course. Notes throughout the text refer the student to the relevant module in the Teacher's Pack, which contains assessments, tests and detailed explanations of the number skills needed to complete the projects.
Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios (International Series in Operations Research & Management Science #306)
by Burcu Adıgüzel MercangözThis book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz’s portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio’s decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset.The book explains PSO in detail and demonstrates how to implement Markowitz’s portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.
Applying Power Series to Differential Equations: An Exploration through Questions and Projects (Problem Books in Mathematics)
by James Sochacki Anthony TongenThis book is aimed to undergraduate STEM majors and to researchers using ordinary differential equations. It covers a wide range of STEM-oriented differential equation problems that can be solved using computational power series methods. Many examples are illustrated with figures and each chapter ends with discovery/research questions most of which are accessible to undergraduate students, and almost all of which may be extended to graduate level research. Methodologies implemented may also be useful for researchers to solve their differential equations analytically or numerically. The textbook can be used as supplementary for undergraduate coursework, graduate research, and for independent study.
Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health)
by Matthew P. Fox Timothy L. Lash Aliza K. FinkThis text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results. Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects.
Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health)
by Matthew P. Fox Richard F. MacLehose Timothy L. LashThis textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.
Applying Respondent Driven Sampling to Migrant Populations: Lessons from the Field
by Lisa G. Johnston Guri TyldumThis book gives a thorough introduction to the theoretical and practical aspects of planning, conducting and analysing data from Respondent Driven Sampling surveys, drawing on the experiences of experts in the field as well as pioneers that have applied Respondent Driven Sampling methodology to migrant populations.
Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses
by Philip GoodThis publication is directed at both attorneys and statisticians to ensure they will work together successfully on the application of statistics in the law. Attorneys will learn how best to utilize the statistician's talents, while gaining an enriched understanding of the law relevant to audits, jury selection, discrimination, environmental hazards, evidence, and torts as it relates to statistical issues. Statisticians will learn that the law is what judges say it is and to frame their arguments accordingly. This book will increase the effectiveness of both parties in presenting and attacking statistical arguments in the courtroom. Topics covered include sample and survey methods, probability, testing hypotheses, and multiple regression.
Applying the Rasch Model: Fundamental Measurement in the Human Sciences
by Trevor Bond Zi Yan Moritz HeeneRecognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background. Highlights of the new edition include: More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.
Applying the Rasch Model and Structural Equation Modeling to Higher Education: The Technology Satisfaction Model
by A.Y.M. Atiquil IslamThis book introduces the fundamentals of the technology satisfaction model (TSM), supporting readers in applying the Rasch model and structural equation modeling (SEM) – a multivariate technique – to higher education (HE) research. User satisfaction is traditionally measured along a single dimension. However, the TSM includes digital technologies for teaching, learning and research across three dimensions: computer efficacy, perceived ease of use and perceived usefulness. Establishing relationships among these factors is a challenge. Although commonly used in psychology to trace relationships, Rasch and SEM approaches are rarely used in educational technology or library and information science. This book, therefore, shows that combining these two analytical tools offers researchers better options for measurement and generalisation in HE research. This title presents theoretical and methodological insights of use to researchers in HE.
Approaches for Science Illustration and Communication (Biomedical Visualization #4)
by Mark RoughleyThis edited book explores the breadth of approaches undertaken by scientists, artists and communicators in their crucial role making science accessible, engaging and impactful. Contemporary approaches in science illustration and visualization include a variety of creative methodologies that are valuable for effective communication, teaching, learning and professional practice. These range in method from anatomical drawings used in medical curricula, to 2D animations and editorial illustrations available in the public realm. They also include unexpected approaches such as the use of tabletop board games, comics and collage in understanding our bodies, emergent health threats and cutting-edge science developments. If you are a scientist seeking to enhance your ability to communicate your research or an artist interested in biomedical visualization, this volume serves as an introduction to contemporary approaches in science illustration and communication. By understanding the creative methods and techniques employed in this field, we can collectively work towards fostering a deeper appreciation of art in science, and continue to captivate and inspire audiences worldwide.
Approaches in Integrative Bioinformatics: Towards the Virtual Cell
by Ming Chen Ralf HofestädtApproaches in Integrative Bioinformatics provides a basic introduction to biological information systems, as well as guidance for the computational analysis of systems biology. This book also covers a range of issues and methods that reveal the multitude of omics data integration types and the relevance that integrative bioinformatics has today. Topics include biological data integration and manipulation, modeling and simulation of metabolic networks, transcriptomics and phenomics, and virtual cell approaches, as well as a number of applications of network biology. It helps to illustrate the value of integrative bioinformatics approaches to the life sciences. This book is intended for researchers and graduate students in the field of Bioinformatics. Professor Ming Chen is the Director of the Bioinformatics Laboratory at the College of Life Sciences, Zhejiang University, Hangzhou, China. Professor Ralf Hofestädt is the Chair of the Department of Bioinformatics and Medical Informatics, Bielefeld University, Germany.
Approaches, Opportunities, and Challenges for Eco-design 4.0: A Concise Guide for Practitioners and Students
by Samira KeivanpourThis book addresses the implications of the Industry 4.0 paradigm in design for the environment. We examine the opportunities for, and challenges of, the implications of cyber-physical systems, big data analytics, Internet of things, additive manufacturing, and simulation in a range of areas in an eco-design context. These include selecting low impact materials, choosing manufacturing processes with environmental considerations, end of life strategies, applying design approaches for disassembly, integrating economic and social components into environmental studies, and stakeholder’s involvement. This volume takes a step toward this journey to explore how the three pillars of technology, sustainability, and evolving consumers could shape the future of the product’s design.
Approaches to Geo-mathematical Modelling: New Tools for Complexity Science
by Alan WilsonGeo-mathematical modelling: models from complexity science Sir Alan Wilson, Centre for Advanced Spatial Analysis, University College London Mathematical and computer models for a complexity science tool kit Geographical systems are characterised by locations, activities at locations, interactions between them and the infrastructures that carry these activities and flows. They can be described at a great variety of scales, from individuals and organisations to countries. Our understanding, often partial, of these entities, and in many cases this understanding is represented in theories and associated mathematical models. In this book, the main examples are models that represent elements of the global system covering such topics as trade, migration, security and development aid together with examples at finer scales. This provides an effective toolkit that can not only be applied to global systems, but more widely in the modelling of complex systems. All complex systems involve nonlinearities involving path dependence and the possibility of phase changes and this makes the mathematical aspects particularly interesting. It is through these mechanisms that new structures can be seen to 'emerge', and hence the current notion of 'emergent behaviour'. The range of models demonstrated include account-based models and biproportional fitting, structural dynamics, space-time statistical analysis, real-time response models, Lotka-Volterra models representing 'war', agent-based models, epidemiology and reaction-diffusion approaches, game theory, network models and finally, integrated models. Geo-mathematical modelling: Presents mathematical models with spatial dimensions. Provides representations of path dependence and phase changes. Illustrates complexity science using models of trade, migration, security and development aid. Demonstrates how generic models from the complexity science tool kit can each be applied in a variety of situations This book is for practitioners and researchers in applied mathematics, geography, economics, and interdisciplinary fields such as regional science and complexity science. It can also be used as the basis of a modelling course for postgraduate students.
Approaches to Qualitative Research in Mathematics Education: Examples of Methodology and Methods (Advances in Mathematics Education)
by Norma Presmeg Angelika Bikner-Ahsbahs Christine KnippingThis volume documents a range of qualitative research approaches emerged within mathematics education over the last three decades, whilst at the same time revealing their underlying methodologies. Continuing the discussion as begun in the two 2003 ZDM issues dedicated to qualitative empirical methods, this book presents astate of the art overview on qualitative research in mathematics education and beyond. The structure of the book allows the reader to use it as an actual guide for the selection of an appropriate methodology, on a basis of both theoretical depth and practical implications. The methods and examples illustrate how different methodologies come to life when applied to a specific question in a specific context. Many of the methodologies described are also applicable outside mathematics education, but the examples provided are chosen so as to situate the approach in a mathematical context.
Approaching Infinity
by Michael HuemerApproaching Infinity addresses seventeen paradoxes of the infinite, most of which have no generally accepted solutions. The book addresses these paradoxes using a new theory of infinity, which entails that an infinite series is uncompletable when it requires something to possess an infinite intensive magnitude. Along the way, the author addresses the nature of numbers, sets, geometric points, and related matters. The book addresses the need for a theory of infinity, and reviews both old and new theories of infinity. It discussing the purposes of studying infinity and the troubles with traditional approaches to the problem, and concludes by offering a solution to some existing paradoxes.
Approaching Multivariate Analysis, 2nd Edition: A Practical Introduction
by Pat Dugard John Todman Harry StainesThis fully updated new edition not only provides an introduction to a range of advanced statistical techniques that are used in psychology, but has been expanded to include new chapters describing methods and examples of particular interest to medical researchers. It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems. This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain an intuitive understanding of what each method is for, what it does and how it does it. The first chapter briefly reviews the main concepts of univariate and bivariate methods and provides an overview of the multivariate methods that will be discussed, bringing out the relationships among them, and summarising how to recognise what types of problem each of them may be appropriate for tackling. In the remaining chapters, introductions to the methods and important conceptual points are followed by the presentation of typical applications from psychology and medicine, using examples with fabricated data. Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, as well as details of how to interpret and report the output, and extracts of SPSS syntax and code from relevant SAS procedures. This book gets students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, medical students and students or academics in any discipline that uses multivariate methods.
Approaching the Kannan-Lovász-Simonovits and Variance Conjectures (Lecture Notes in Mathematics #2131)
by David Alonso-Gutiérrez Jesús BasteroFocusing on two central conjectures of Asymptotic Geometric Analysis, the Kannan-Lovász-Simonovits spectral gap conjecture and the variance conjecture, these Lecture Notes present the theory in an accessible way, so that interested readers, even those who are not experts in the field, will be able to appreciate the treated topics. Offering a presentation suitable for professionals with little background in analysis, geometry or probability, the work goes directly to the connection between isoperimetric-type inequalities and functional inequalities, giving the interested reader rapid access to the core of these conjectures. In addition, four recent and important results in this theory are presented in a compelling way. The first two are theorems due to Eldan-Klartag and Ball-Nguyen, relating the variance and the KLS conjectures, respectively, to the hyperplane conjecture. Next, the main ideas needed prove the best known estimate for the thin-shell width given by Guédon-Milman and an approach to Eldan's work on the connection between the thin-shell width and the KLS conjecture are detailed.
Approximate Dynamic Programming
by Warren B. PowellPraise for the First Edition"Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners."--Computing ReviewsThis new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problemsUnderstanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplines--Markov decision processes, mathematical programming, simulation, and statistics--to demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP.The book continues to bridge the gap between computer science, simulation, and operations research and now adopts the notation and vocabulary of reinforcement learning as well as stochastic search and simulation optimization. The author outlines the essential algorithms that serve as a starting point in the design of practical solutions for real problems. The three curses of dimensionality that impact complex problems are introduced and detailed coverage of implementation challenges is provided. The Second Edition also features:A new chapter describing four fundamental classes of policies for working with diverse stochastic optimization problems: myopic policies, look-ahead policies, policy function approximations, and policies based on value function approximationsA new chapter on policy search that brings together stochastic search and simulation optimization concepts and introduces a new class of optimal learning strategiesUpdated coverage of the exploration exploitation problem in ADP, now including a recently developed method for doing active learning in the presence of a physical state, using the concept of the knowledge gradientA new sequence of chapters describing statistical methods for approximating value functions, estimating the value of a fixed policy, and value function approximation while searching for optimal policiesThe presented coverage of ADP emphasizes models and algorithms, focusing on related applications and computation while also discussing the theoretical side of the topic that explores proofs of convergence and rate of convergence. A related website features an ongoing discussion of the evolving fields of approximation dynamic programming and reinforcement learning, along with additional readings, software, and datasets.Requiring only a basic understanding of statistics and probability, Approximate Dynamic Programming, Second Edition is an excellent book for industrial engineering and operations research courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who utilize dynamic programming, stochastic programming, and control theory to solve problems in their everyday work.
Approximate Fixed Points of Nonexpansive Mappings (Developments in Mathematics #80)
by Alexander J. ZaslavskiFixed point theory of nonlinear operators has been a rapidly growing area of research and plays an important role in the study of variational inequalities, monotone operators, feasibility problems, and optimization theory, to name just several. This book discusses iteration processes associated with a given nonlinear mapping which generate its approximate fixed point and in some cases converge to a fixed point of the mapping. Various classes of nonlinear single-valued and set-valued mappings are considered along with iteration processes under the presence of computational errors. Of particular interest to mathematicians working in fixed point theory and nonlinear analysis, the added value for the reader are the solutions presented to a number of difficult problems in the fixed point theory which have important applications.