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Spatial Data Analysis in Ecology and Agriculture Using R
Since the publication of the second edition of Richard Plant’s bestselling textbook "Spatial Data Analysis in Ecology and Agriculture Using R", the methodology of spatial data analysis and the suite of R tools for carrying out this analysis have evolved dramatically. This third edition thus explores both the leading software tools for the analysis of vector and raster data; the first based on sf and associated libraries, the second based on the terra package as it has evolved out of the earlier raster package.Further, within the methodology of spatial data analysis, the set of methods available has significantly expanded. This book adds several of the most popular and useful, including machine learning methods in spatial data analysis, the use of simulation methods in spatial data analysis, and a new chapter on the analysis of remotely sensed data. These methods are critically compared in the context of addressing the particular goals of the research project.The book’s practical coverage of spatial statistics, real-world examples, and user-friendly approach make this an essential textbook for ecology and agriculture graduate students. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.Additional material to accompany the book, including a review of mathematical concepts, the full data sets, and a brief introduction to geographic coordinate systems, can be accessed via the Instructor Resources link on www.routledge.com.
R 5,134.14
Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
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R 519.00
Specification Analysis in the Linear Model
Originally published in 1987. This collection of original papers deals with various issues of specification in the context of the linear statistical model. The volume honours the early econometric work of Donald Cochrane, late Dean of Economics and Politics at Monash University in Australia. The chapters focus on problems associated with autocorrelation of the error term in the linear regression model and include appraisals of early work on this topic by Cochrane and Orcutt. The book includes an extensive survey of autocorrelation tests; some exact finite-sample tests; and some issues in preliminary test estimation. A wide range of other specification issues is discussed, including the implications of random regressors for Bayesian prediction; modelling with joint conditional probability functions; and results from duality theory. There is a major survey chapter dealing with specification tests for non-nested models, and some of the applications discussed by the contributors deal with the British National Accounts and with Australian financial and housing markets.
R 1,246.00
Evaluation Essentials, Third Edition
Beloved for its conversational style and reliable advice, this text is now in a revised and updated third edition, reflecting key developments in evaluation. It includes expanded coverage of equity and social justice issues, values and cost analysis, visualizing qualitative data with software, and more. Twenty-six concise chapters, or "sessions," give students, applied researchers, and program administrators a solid foundation for conducting or using evaluations. Covering both quantitative and qualitative methods, the book emphasizes fostering evaluation use. It shows how to build collaborative relationships with users; formulate answerable evaluation questions; deal with contingencies that might alter the traditional sequence of an evaluation; and collect, analyze, and report data. Student-friendly features throughout the sessions include titles written as questions, bulleted recaps, "Thinking Ahead" and "Next Steps" pointers, cautionary notes, and annotated suggestions for further reading. An in-depth case study provides the basis for end-of-session practice exercises. New to This Edition *New sessions on context-sensitive evaluation, including the organizational, sociopolitical, and community contexts of a program. *New or expanded discussions of timely topics: identifying evaluation decision makers, analyzing program costs, coding and visualizing qualitative data with software, and more. *Updated suggestions for further reading and discussion in every chapter.
R 2,125.00
Scientific Data Analysis using Jython Scripting and Java
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
R 1,582.96
Multicriteria Portfolio Construction with Python
This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub.This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions.The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.
R 3,956.14
Methods of Multivariate Analysis
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor AnalysisClassification TreesDynamic GraphicsTransformations to NormalityPrediction for Multivariate Multiple RegressionKronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.
R 4,851.00
Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
R 1,335.00
Differential Analysis on Complex Manifolds
A brand new appendix by Oscar Garcia-Prada graces this third edition of a classic work. In developing the tools necessary for the study of complex manifolds, this comprehensive, well-organized treatment presents in its opening chapters a detailed survey of recent progress in four areas: geometry (manifolds with vector bundles), algebraic topology, differential geometry, and partial differential equations. Wells’s superb analysis also gives details of the Hodge-Riemann bilinear relations on Kahler manifolds, Griffiths's period mapping, quadratic transformations, and Kodaira's vanishing and embedding theorems. Oscar Garcia-Prada’s appendix gives an overview of the developments in the field during the decades since the book appeared.
R 1,826.91
Analysis
Hauptthema dieses zweiten Bandes ist die Differential- und Integralrechnung für Funktionen von mehreren Veränderlichen. Dabei wird auch das Lebesguesche Integral im Rn behandelt. Dem erfolgreichen Konzept von "Analysis 1" folgend, wird viel Wert auf historische Zusammenhänge, Ausblicke und die Entwicklung der Analysis gelegt. Zu den Besonderheiten, die über den kanonischen Stoff des zweiten Semesters hinausgehen, gehören das Morsesche und das Sardsche Lemma, die C?-Approximation von Funktionen (Mollifiers) und die Theorie der absolutstetigen Funktionen. Zahlreiche Beispiele, Übungsaufgaben und Anwendungen, z.B. aus der Physik und Astronomie, runden dieses Lehrbuch ab. Der Abschnitt "Lösungen und Lösungshinweise" wurde für die Neuauflage wesentlich erweitert, so daß die überwiegende Zahl der Aufgaben im Buch nun besprochen oder vollständig gelöst wird.
R 2,003.63
Sensitivity Analysis in Practice
Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors. Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB -- freely available sensitivity analysis software. Provides an accessible overview of the most widely used sensitivity analysis methods. Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering. Opens with a detailed worked example to explain the motivation behind methods described.Includes a range
R 3,906.00
Bioimage Data Analysis Workflows ‒ Advanced Components and Methods
This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images.
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R 611.00
R 1,221.00
Stochastic Analysis and Applications
This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and stochastic problems in physics and biology. It includes information on the theory of Dirichlet forms, Feynman integration and the Schrodinger's equation.
R 3,737.84
Primer of Genetic Analysis
An invaluable student-tested study aid, this primer, first published in 2007, provides guided instruction for the analysis and interpretation of genetic principles and practice in problem solving. Each section is introduced with a summary of useful hints for problem solving and an overview of the topic with key terms. A series of problems, generally progressing from simple to more complex, then allows students to test their understanding of the material. Each question and answer is accompanied by detailed explanation. This third edition includes additional problems in basic areas that often challenge students, extended coverage in molecular biology and development, an expanded glossary of terms, and updated historical landmarks. Students at all levels, from beginning biologists and premedical students to graduates seeking a review of basic genetics, will find this book a valuable aid. It will complement the formal presentation in any genetics textbook or stand alone as a self-paced review manual.
R 2,759.38
Basin Analysis
Basin Analysis is an advanced undergraduate and postgraduate text aimed at understanding sedimentary basins as geodynamic entities. The rationale of the book is that knowledge of the basic principles of the thermo-mechanical behaviour of the lithosphere, the dynamics of the mantle, and the functioning of sediment routing systems provides a sound background for studying sedimentary basins, and is a pre-requisite for the exploitation of resources contained in their sedimentary rocks. The third edition incorporates new developments in the burgeoning field of basin analysis while retaining the successful structure and overall philosophy of the first two editions. The text is divided into 4 parts that establish the geodynamical environment for sedimentary basins and the physical state of the lithosphere, followed by a coverage of the mechanics of basin formation, an integrated analysis of the controls on the basin-fill and its burial and thermal history, and concludes with an application of basin analysis principles in petroleum play assessment, including a discussion of unconventional hydrocarbon plays. The text is richly supplemented by Appendices providing mathematical derivations of a wide range of processes affecting the formation of basins and their sedimentary fills. Many of these Appendices include practical exercises that give the reader hands-on experience of quantitative solutions to important basin analysis processes. Now in full colour and a larger format, this third edition is a comprehensive update and expansion of the previous editions, and represents a rigorous yet accessible guide to problem solving in this most integrative of geoscientific disciplines. Additional resources for this book can be found at: www.wiley.com/go/allen/basinanalysis.
R 3,353.00
Occupational and Activity Analysis
Newly updated to reflect the Occupational Therapy Practice Framework: Domain and Process, Fourth Edition (OTPF-4), Occupational and Activity Analysis, Third Edition outlines the process of conducting occupational and activity analyses for occupational therapy students and clinicians.Occupational therapy practitioners use occupations and activities not only as a goal but also as a treatment medium, so understanding both the uniqueness of a client’s occupations and how an activity can be used therapeutically is essential. This text is an introduction to both realms, first by explaining the process by which to peel back the layers of an occupation or activity to reveal its intricacy and then examining how to use this information for evaluation and intervention.Dr. Heather Thomas has updated Occupational and Activity Analysis, Third Edition to reflect the significant changes made to the activity analysis process and terminology in the OTPF-4. Conducting either an occupational or activity analysis investigates not only what is required for full participation but also looks at the meaning ascribed to it by the people, groups, or communities engaging in it and how personal and environmental contexts impact participation.What’s new and included in the Third Edition:Chapters throughout the text have been updated to reflect the changes in the OTPF-4.Updated educational standards set by the Accreditation Council for Occupational Therapy Education (ACOTE) for doctoral and master’s level and occupational therapy assistant programs.Additional cases and relevant clinical examples.Updated tables, boxes, and figures throughout.Expanded section on communication management.Appendices containing updated occupational and activity analysis forms.Example of a full activity analysis.Updated and added photos to help students understand concepts.Instructors in educational settings can visit www.efacultylounge.com for forms, videos, and PowerPoint presentations to be used for teaching in the classroom.As a foundational skill, occupational and activity analysis is utilized throughout students’ careers and into their lives as practitioners, making Occupational and Activity Analysis, Third Edition the perfect textbook for the occupational therapy or occupational therapy assistant student, faculty, or clinician.
R 3,757.73
Analysis of Doubly Truncated Data
This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.
R 2,015.00
Evaluation Essentials, Third Edition
Beloved for its conversational style and reliable advice, this text is now in a revised and updated third edition, reflecting key developments in evaluation. It includes expanded coverage of equity and social justice issues, values and cost analysis, visualizing qualitative data with software, and more. Twenty-six concise chapters, or "sessions," give students, applied researchers, and program administrators a solid foundation for conducting or using evaluations. Covering both quantitative and qualitative methods, the book emphasizes fostering evaluation use. It shows how to build collaborative relationships with users; formulate answerable evaluation questions; deal with contingencies that might alter the traditional sequence of an evaluation; and collect, analyze, and report data. Student-friendly features throughout the sessions include titles written as questions, bulleted recaps, "Thinking Ahead" and "Next Steps" pointers, cautionary notes, and annotated suggestions for further reading. An in-depth case study provides the basis for end-of-session practice exercises. New to This Edition *New sessions on context-sensitive evaluation, including the organizational, sociopolitical, and community contexts of a program. *New or expanded discussions of timely topics: identifying evaluation decision makers, analyzing program costs, coding and visualizing qualitative data with software, and more. *Updated suggestions for further reading and discussion in every chapter.
R 3,210.00
Sparse Estimation with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762)Sparse Estimation with Math and R
R 1,649.00
Lectures on Advanced Topics in Categorical Data Analysis
This book continues the mission of the previous text by the author, Lectures on Categorical Data Analysis, by expanding on the introductory concepts from that volume and providing a mathematically rigorous presentation of advanced topics and current research in statistical techniques which can be applied in the social, political, behavioral, and life sciences. It presents an intuitive and unified discussion of an array of themes in categorical data analysis, and the emphasis on structure over stochastics renders many of the methods applicable in machine learning environments and for the analysis of big data.The book focuses on graphical models, their application in causal analysis, the analytical properties of parameterizations of multivariate discrete distributions, marginal models, and coordinate-free relational models. To guide the readers in future research, the volume provides references to original papers and also offers detailed proofs of most of the significant results. Like the previous volume, it features exercises and research questions, making it appropriate for graduate students, as well as for active researchers.
R 4,735.73
Analysis for Computer Scientists
This easy-to-follow textbook/reference presents a concise introduction to mathematical analysis from an algorithmic point of view, with a particular focus on applications of analysis and aspects of mathematical modelling. The text describes the mathematical theory alongside the basic concepts and methods of numerical analysis, enriched by computer experiments using MATLAB, Python, Maple, and Java applets. This fully updated and expanded new edition also features an even greater number of programming exercises.Topics and features: describes the fundamental concepts in analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives, integrals, and curves; discusses important applications and advanced topics, such as fractals and L-systems, numerical integration, linear regression, and differential equations; presents tools from vector and matrix algebra in the appendices, together with further information on continuity; includes added material on hyperbolic functions, curves and surfaces in space, second-order differential equations, and the pendulum equation (NEW); contains experiments, exercises, definitions, and propositions throughout the text; supplies programming examples in Python, in addition to MATLAB (NEW); provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material.Addressing the core needs of computer science students and researchers, this clearly written textbook is an essential resource for undergraduate-level courses on numerical analysis, and an ideal self-study tool for professionals seeking to enhance their analysis skills.
R 2,379.11
Numeric Computation and Statistical Data Analysis on the Java Platform
Numericalcomputation, knowledge discovery and statistical data analysis integrated withpowerful 2D and 3D graphics for visualization are the key topics of this book. ThePython code examples powered by the Java platform can easily be transformed toother programming languages, such as Java, Groovy, Ruby and BeanShell. Thisbook equips the reader with acomputational platform which, unlike other statistical programs, is not limitedby a single programming language.The authorfocuses on practical programming aspects and covers a broad range of topics,from basic introduction to the Python language on the Java platform (Jython),to descriptive statistics, symbolic calculations, neural networks, non-linearregression analysis and many other data-mining topics. He discusses how to findregularities in real-world data, how to classify data, and how to process datafor knowledge discoveries. The code snippets are so short that they easily fit intosingle pages.Numeric Computation and Statistical DataAnalysis on the Java Platform is a great choice for those who want to learn how statisticaldata analysis can be done using popular programming languages, who want tointegrate data analysis algorithms in full-scale applications, and deploy suchcalculations on the web pages or computational servers regardlessof their operating system. It is an excellent reference for scientific computations to solvereal-world problems using a comprehensive stack of open-source Javalibraries included in the DataMelt (DMelt) project and will beappreciated by many data-analysis scientists, engineers and students.
R 4,396.00
Posteriori Error Analysis Via Duality Theory
This work provides a posteriori error analysis for mathematical idealizations in modeling boundary value problems, especially those arising in mechanical applications, and for numerical approximations of numerous nonlinear var- tional problems. An error estimate is called a posteriori if the computed solution is used in assessing its accuracy. A posteriori error estimation is central to m- suring, controlling and minimizing errors in modeling and numerical appr- imations. In this book, the main mathematical tool for the developments of a posteriori error estimates is the duality theory of convex analysis, documented in the well-known book by Ekeland and Temam ([49]). The duality theory has been found useful in mathematical programming, mechanics, numerical analysis, etc. The book is divided into six chapters. The first chapter reviews some basic notions and results from functional analysis, boundary value problems, elliptic variational inequalities, and finite element approximations. The most relevant part of the duality theory and convex analysis is briefly reviewed in Chapter 2.
R 3,040.24
Statistical Data Analysis for the Physical Sciences
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
R 1,224.39
Modeling and Analysis of Dynamic Systems
The third edition of Modeling and Anaysis of Dynamic Systems continues to present students with the methodology applicable to the modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems. Models are developed in the form of state-variable equations, input-output differential equations, transfer functions, and block diagrams. The Laplace transform is used for analytical solutions. Computer solutions are based on MATLAB and Simulink. Examples include both linear and nonlinear systems. An introduction is given to the modeling and design tools for feedback control systems. The text offers considerable flexibility in the selection of material for a specific course. Students majoring in many different engineering disciplines have used the text. Such courses are frequently followed by control-system design courses in the various disciplines.
R 8,267.00
Genomics Data Analysis
Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
R 1,246.00
Hands-On Simulation Modeling with Python
Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guideKey FeaturesLearn to create a digital prototype of a real model using hands-on examplesEvaluate the performance and output of your prototype using simulation modeling techniquesUnderstand various statistical and physical simulations to improve systems using PythonBook DescriptionSimulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGain an overview of the different types of simulation modelsGet to grips with the concepts of randomness and data generation processUnderstand how to work with discrete and continuous distributionsWork with Monte Carlo simulations to calculate a definite integralFind out how to simulate random walks using Markov chainsObtain robust estimates of confidence intervals and standard errors of population parametersDiscover how to use optimization methods in real-life applicationsRun efficient simulations to analyze real-world systemsWho this book is forHands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
R 2,214.00
Introduction to Computer-Intensive Methods of Data Analysis in Biology
This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
R 2,733.04
Introduction to Computer-Intensive Methods of Data Analysis in Biology
This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
R 6,644.64
Bayesian Optimization with Application to Computer Experiments
This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field.This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
R 3,475.58
Stochastic Analysis for Finance with Simulations
This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoretical concepts. Stochastic Analysis for Finance with Simulations is designed for readers who want to have a deeper understanding of the delicate theory of quantitative finance by doing computer simulations in addition to theoretical study. It will particularly appeal to advanced undergraduate and graduate students in mathematics and business, but not excluding practitioners in finance industry.
R 3,297.00
SPSS Companion for the Third Edition of The Fundamentals of Political Science Research
An SPSS Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of SPSS using real political science data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using SPSS. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of SPSS step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in SPSS to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses.
R 935.54
R 1,527.00
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height
This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data.This monograph is a research book and it is partly cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, that methodology has been applied to a problem in the field of physical oceanography, analyzing data for significant wave height, which is of crucial importance to ocean engineering disciplines. Indeed, the statistical properties of significant wave height are important for the design, construction and operation of ships and other marine and coastal structures. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what that effect might be. Thus, this book is an important contribution to the ongoing debate on climate change, its implications and how to adapt to a changing climate, with a particular focus on the maritime industries and the marine environment. This book should be of value to anyone with an interest in the statistical modelling of environmental processes, and in particular to those with an interest in the ocean wave climate. It is written on a level that should be understandable to everyone with a basic background in statistics or elementary mathematics, and an introduction to some basic concepts is provided in the appendices for the uninitiated reader. The intended readership includes students and professionals involved in statistics, oceanography, ocean engineering, environmental research, climate sciences and risk assessment. Moreover, the book’s findings are relevant for various stakeholders in the maritime industries such as design offices, classification societies, ship owners, yards and operators, flag states and intergovernmental agencies such as the IMO.
R 2,154.61
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.
R 5,644.78
Experimental Design and Data Analysis for Biologists
Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.
R 2,437.00
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes.Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives.The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed.The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.
R 3,339.80
Computational Analysis
Featuring the clearly presented and expertly-refereed contributions of leading researchers in the field of approximation theory, this volume is a collection of the best contributions at the Third International Conference on Applied Mathematics and Approximation Theory, an international conference held at TOBB University of Economics and Technology in Ankara, Turkey, on May 28-31, 2015.The goal of the conference, and this volume, is to bring together key work from researchers in all areas of approximation theory, covering topics such as ODEs, PDEs, difference equations, applied analysis, computational analysis, signal theory, positive operators, statistical approximation, fuzzy approximation, fractional analysis, semigroups, inequalities, special functions and summability. These topics are presented both within their traditional context of approximation theory, while also focusing on their connections to applied mathematics. As a result, this collection will be an invaluable resource for researchers in applied mathematics, engineering and statistics.
R 6,020.38
Biomedical Signal Analysis
Comprehensive resource covering recent developments, applications of current interest, and advanced techniques for biomedical signal analysis Biomedical Signal Analysis provides extensive insight into digital signal processing techniques for filtering, identification, characterization, classification, and analysis of biomedical signals with the aim of computer-aided diagnosis, taking a unique approach by presenting case studies encountered in the authors’ research work. Each chapter begins with the statement of a biomedical signal problem, followed by a selection of real-life case studies and illustrations with the associated signals. Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple "textbook" methods, followed by more sophisticated research-informed approaches. Each chapter concludes with solutions to practical applications. Illustrations of real-life biomedical signals and their derivatives are included throughout. The third edition expands on essential background material and advanced topics without altering the underlying pedagogical approach and philosophy of the successful first and second editions. The book is enhanced by a large number of study questions and laboratory exercises as well as an online repository with solutions to problems and data files for laboratory work and projects. Biomedical Signal Analysis provides theoretical and practical information on: The origin and characteristics of several biomedical signalsAnalysis of concurrent, coupled, and correlated processes, with applications in monitoring of sleep apneaFiltering for removal of artifacts, random noise, structured noise, and physiological interference in signals generated by stationary, nonstationary, and cyclostationary processesDetection and characterization of events, covering methods for QRS detection, identification of heart sounds, and detection of the dicrotic notchAnalysis of waveshape and waveform complexityInterpretation and analysis of biomedical signals in the frequency domainMathematical, electrical, mechanical, and physiological modeling of biomedical signals and systemsSophisticated analysis of nonstationary, multicomponent, and multisource signals using wavelets, time-frequency representations, signal decomposition, and dictionary-learning methods Pattern classification and computer-aided diagnosis Biomedical Signal Analysis is an ideal learning resource for senior undergraduate and graduate engineering students. Introductory sections on signals, systems, and transforms make this book accessible to students in disciplines other than electrical engineering.
R 5,373.00
R 1,994.94
R 565.35
Stochastic Analysis in Discrete and Continuous Settings
This monograph is an introduction to some aspects of stochastic analysis in the framework of normal martingales, in both discrete and continuous time. The text is mostly self-contained, except for Section 5.7 that requires some background in geometry, and should be accessible to graduate students and researchers having already received a basic training in probability. Prereq- sites are mostly limited to a knowledge of measure theory and probability, namely?-algebras,expectations,andconditionalexpectations.Ashortint- duction to stochastic calculus for continuous and jump processes is given in Chapter 2 using normal martingales, whose predictable quadratic variation is the Lebesgue measure. There already exists several books devoted to stochastic analysis for c- tinuous di?usion processes on Gaussian and Wiener spaces, cf. e.g. [51], [63], [65], [72], [83], [84], [92], [128], [134], [143], [146], [147]. The particular f- ture of this text is to simultaneously consider continuous processes and jump processes in the uni?ed framework of normal martingales.
R 1,560.93
