deterministic walk on random graph data fusion and emergence of patterns



Bendat Julius S. Random Data. Analysis and Measurement Procedures Bendat Julius S. Random Data. Analysis and Measurement Procedures Новинка

Bendat Julius S. Random Data. Analysis and Measurement Procedures

14160.49 руб. или Купить в рассрочку!
A timely update of the classic book on the theory and application of random data analysis First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-undergraduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data.
Oliver Ibe C. Elements of Random Walk and Diffusion Processes Oliver Ibe C. Elements of Random Walk and Diffusion Processes Новинка

Oliver Ibe C. Elements of Random Walk and Diffusion Processes

Presents an important and unique introduction to random walk theory Random walk is a stochastic process that has proven to be a useful model in understanding discrete-state discrete-time processes across a wide spectrum of scientific disciplines. Elements of Random Walk and Diffusion Processes provides an interdisciplinary approach by including numerous practical examples and exercises with real-world applications in operations research, economics, engineering, and physics. Featuring an introduction to powerful and general techniques that are used in the application of physical and dynamic processes, the book presents the connections between diffusion equations and random motion. Standard methods and applications of Brownian motion are addressed in addition to Levy motion, which has become popular in random searches in a variety of fields. The book also covers fractional calculus and introduces percolation theory and its relationship to diffusion processes. With a strong emphasis on the relationship between random walk theory and diffusion processes, Elements of Random Walk and Diffusion Processes features: Basic concepts in probability, an overview of stochastic and fractional processes, and elements of graph theory Numerous practical applications of random walk across various disciplines, including how to model stock prices and gambling, describe the statistical properties of genetic drift, and simplify the random movement of molecules in liquids and gases Examples of the real-world applicability of random walk such as node movement and node failure in wireless networking, the size of the Web in computer science, and polymers in physics Plentiful examples and exercises throughout that illustrate the solution of many practical problems Elements of Random Walk and Diffusion Processes is an ideal reference for researchers and professionals involved in operations research, economics, engineering, mathematics, and physics. The book is also an excellent textbook for upper-undergraduate and graduate level courses in probability and stochastic processes, stochastic models, random motion and Brownian theory, random walk theory, and diffusion process techniques.
Dehmer Matthias Statistical and Machine Learning Approaches for Network Analysis Dehmer Matthias Statistical and Machine Learning Approaches for Network Analysis Новинка

Dehmer Matthias Statistical and Machine Learning Approaches for Network Analysis

9720.99 руб. или Купить в рассрочку!
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
Richard Brath Graph Analysis and Visualization. Discovering Business Opportunity in Linked Data Richard Brath Graph Analysis and Visualization. Discovering Business Opportunity in Linked Data Новинка

Richard Brath Graph Analysis and Visualization. Discovering Business Opportunity in Linked Data

Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences – until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative resource.
Otto Wildi Data Analysis in Vegetation Ecology Otto Wildi Data Analysis in Vegetation Ecology Новинка

Otto Wildi Data Analysis in Vegetation Ecology

11477.65 руб. или Купить в рассрочку!
Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.
Luca Massaron Python for Data Science For Dummies Luca Massaron Python for Data Science For Dummies Новинка

Luca Massaron Python for Data Science For Dummies

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Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Hyunjoung Lee Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee Fundamentals of Big Data Network Analysis for Research and Industry Новинка

Hyunjoung Lee Fundamentals of Big Data Network Analysis for Research and Industry

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Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book: Explains the basic concepts in understanding big data and filtering meaningful data Presents big data analysis within the networking perspective Features methodology applicable to research and industry Describes in detail the social relationship between big data and its implications Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.
Alain Appriou Uncertainty Theories and Multisensor Data Fusion Alain Appriou Uncertainty Theories and Multisensor Data Fusion Новинка

Alain Appriou Uncertainty Theories and Multisensor Data Fusion

11339.09 руб. или Купить в рассрочку!
Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.
Xavier Lorca Tree-based Graph Partitioning Constraint Xavier Lorca Tree-based Graph Partitioning Constraint Новинка

Xavier Lorca Tree-based Graph Partitioning Constraint

Combinatorial problems based on graph partitioning enable us to mathematically represent and model many practical applications. Mission planning and the routing problems occurring in logistics perfectly illustrate two such examples. Nevertheless, these problems are not based on the same partitioning pattern: generally, patterns like cycles, paths, or trees are distinguished. Moreover, the practical applications are often not limited to theoretical problems like the Hamiltonian path problem, or K-node disjoint path problems. Indeed, they usually combine the graph partitioning problem with several restrictions related to the topology of nodes and arcs. The diversity of implied constraints in real-life applications is a practical limit to the resolution of such problems by approaches considering the partitioning problem independently from each additional restriction. This book focuses on constraint satisfaction problems related to tree partitioning problems enriched by several additional constraints that restrict the possible partitions topology. On the one hand, this title focuses on the structural properties of tree partitioning constraints. On the other hand, it is dedicated to the interactions between the tree partitioning problem and classical restrictions (such as precedence relations or incomparability relations between nodes) involved in practical applications. Precisely, Tree-based Graph Partitioning Constraint shows how to globally take into account several restrictions within one single tree partitioning constraint. Another interesting aspect of this book is related to the implementation of such a constraint. In the context of graph-based global constraints, the book illustrates how a fully dynamic management of data structures makes the runtime of filtering algorithms independent of the graph density.
Alan Anderson Statistics for Big Data For Dummies Alan Anderson Statistics for Big Data For Dummies Новинка

Alan Anderson Statistics for Big Data For Dummies

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The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word «statistics» alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.
Romain Azais Statistical Inference for Piecewise-deterministic Markov Processes Romain Azais Statistical Inference for Piecewise-deterministic Markov Processes Новинка

Romain Azais Statistical Inference for Piecewise-deterministic Markov Processes

9248.97 руб. или Купить в рассрочку!
Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance… Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial. Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as well as applied models. The present book also dwells on statistics in the context of Markov chains, since piecewise-deterministic Markov processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.
Stephanos Kyrkanides Understanding Masticatory Function in Unilateral Crossbites Stephanos Kyrkanides Understanding Masticatory Function in Unilateral Crossbites Новинка

Stephanos Kyrkanides Understanding Masticatory Function in Unilateral Crossbites

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Unilateral posterior crossbite is a problem often seen in orthodontic practice, and properly understanding chewing patterns will lead to the most effective treatment program. Drawing on their research and available literature, Drs. Piancino and Kyrkanides present a fascinating look at chewing cycles and their role in the functional treatment of unilateral posterior crossbite. Describes the physiology and pathology of chewing patterns and muscular activation in humans Explains chewing patterns and muscular coordination, and their influence on the growth and harmony of the stomatognathic system Clinical instruction for checking and correcting masticatory function and functional asymmetry in order to prevent the relapse of the malocclusion Clinical cases walk readers through the treatment of seven crossbites
Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining Новинка

Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining

7114.69 руб. или Купить в рассрочку!
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Eva Murray #MakeoverMonday. Improving How We Visualize and Analyze Data, One Chart at a Time Eva Murray #MakeoverMonday. Improving How We Visualize and Analyze Data, One Chart at a Time Новинка

Eva Murray #MakeoverMonday. Improving How We Visualize and Analyze Data, One Chart at a Time

Explore different perspectives and approaches to create more effective visualizations #MakeoverMonday offers inspiration and a giant dose of perspective for those who communicate data. Originally a small project in the data visualization community, #MakeoverMonday features a weekly chart or graph and a dataset that community members reimagine in order to make it more effective. The results have been astounding; hundreds of people have contributed thousands of makeovers, perfectly illustrating the highly variable nature of data visualization. Different takes on the same data showed a wide variation of theme, focus, content, and design, with side-by-side comparisons throwing more- and less-effective techniques into sharp relief. This book is an extension of that project, featuring a variety of makeovers that showcase various approaches to data communication and a focus on the analytical, design and storytelling skills that have been developed through #MakeoverMonday. Paging through the makeovers ignites immediate inspiration for your own work, provides insight into different perspectives, and highlights the techniques that truly make an impact. Explore the many approaches to visual data communication Think beyond the data and consider audience, stakeholders, and message Design your graphs to be intuitive and more communicative Assess the impact of layout, color, font, chart type, and other design choices Creating visual representation of complex datasets is tricky. There’s the mandate to include all relevant data in a clean, readable format that best illustrates what the data is saying—but there is also the designer’s impetus to showcase a command of the complexity and create multidimensional visualizations that “look cool.” #MakeoverMonday shows you the many ways to walk the line between simple reporting and design artistry to create exactly the visualization the situation requires.
Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties Новинка

Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties

8875.18 руб. или Купить в рассрочку!
In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications. Both recent and more established methods are illustrated to assess many common assumptions on these functions, such as, isotropy, separability, symmetry, and intrinsic correlation. After an extensive introduction to spatial methodology, the book details the effects of common covariance assumptions and addresses methods to assess the appropriateness of such assumptions for various data structures. Key features: An extensive introduction to spatial methodology including a survey of spatial covariance functions and their use in spatial prediction (kriging) is given. Explores methodology for assessing the appropriateness of assumptions on covariance functions in the spatial, spatio-temporal, multivariate spatial, and point pattern settings. Provides illustrations of all methods based on data and simulation experiments to demonstrate all methodology and guide to proper usage of all methods. Presents a brief survey of spatial and spatio-temporal models, highlighting the Gaussian case and the binary data setting, along with the different methodologies for estimation and model fitting for these two data structures. Discusses models that allow for anisotropic and nonseparable behaviour in covariance functions in the spatial, spatio-temporal and multivariate settings. Gives an introduction to point pattern models, including testing for randomness, and fitting regular and clustered point patterns. The importance and assessment of isotropy of point patterns is detailed. Statisticians, researchers, and data analysts working with spatial and space-time data will benefit from this book as well as will graduate students with a background in basic statistics following courses in engineering, quantitative ecology or atmospheric science.
Michel Rigo Advanced Graph Theory and Combinatorics Michel Rigo Advanced Graph Theory and Combinatorics Новинка

Michel Rigo Advanced Graph Theory and Combinatorics

10961.12 руб. или Купить в рассрочку!
Advanced Graph Theory focuses on some of the main notions arising in graph theory with an emphasis from the very start of the book on the possible applications of the theory and the fruitful links existing with linear algebra. The second part of the book covers basic material related to linear recurrence relations with application to counting and the asymptotic estimate of the rate of growth of a sequence satisfying a recurrence relation.
Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics Новинка

Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

7973.25 руб. или Купить в рассрочку!
Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent “how-to” book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks.
Rudis Bob Data-Driven Security. Analysis, Visualization and Dashboards Rudis Bob Data-Driven Security. Analysis, Visualization and Dashboards Новинка

Rudis Bob Data-Driven Security. Analysis, Visualization and Dashboards

3827.16 руб. или Купить в рассрочку!
Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful ? data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards.
Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research Новинка

Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research

9827.93 руб. или Купить в рассрочку!
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Programming Massively Parallel Processors: A Hands-on Approach Programming Massively Parallel Processors: A Hands-on Approach Новинка

Programming Massively Parallel Processors: A Hands-on Approach

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, parallel patterns, and dynamic parallelism are covered in depth. The new edition includes updated coverage of CUDA, including the newer libraries such as CuDNN. New chapters on frequently used parallel patterns have been added, and case studies have been updated to reflect current industry practices. NEW FOR THE THIRD EDITION - Parallel Patterns Includes several new chapters on frequently used parallel patterns (histogram, merge sort, and graph search). - Deep Learning A new chapter on deep learning has been added as an application case study. - Advanced CUDA Features The advanced features of CUDA are explored in a new chapter. - Pascal Recent GPU architectural features are covered, including Pascal.
Maurice Charbit Digital Signal and Image Processing using MATLAB, Volume 2. Advances and Applications: The Deterministic Case Maurice Charbit Digital Signal and Image Processing using MATLAB, Volume 2. Advances and Applications: The Deterministic Case Новинка

Maurice Charbit Digital Signal and Image Processing using MATLAB, Volume 2. Advances and Applications: The Deterministic Case

8240.03 руб. или Купить в рассрочку!
The most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals, the theory being supported by exercises and computer simulations relating to real applications. More than 200 programs and functions are provided in the MATLAB® language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject. Following on from the first volume, this second installation takes a more practical stance, providing readers with the applications of ISP.
Signals and Systems Signals and Systems Новинка

Signals and Systems

This book provides a rigorous treatment of deterministic and random signals. It offers detailed information on topics including random signals, system modelling and system analysis. System analysis in frequency domain using Fourier transform and Laplace transform is explained with theory and numerical problems. The advanced techniques used for signal processing, especially for speech and image processing, are discussed. The properties of continuous time and discrete time signals are explained with a number of numerical problems. The physical significance of different properties is explained using real-life examples. To aid understanding, concept check questions, review questions, a summary of important concepts, and frequently asked questions are included. MATLAB programs, with output plots and simulation examples, are provided for each concept. Students can execute these simulations and verify the outputs.
Elaine Knuth Trading Between the Lines. Pattern Recognition and Visualization of Markets Elaine Knuth Trading Between the Lines. Pattern Recognition and Visualization of Markets Новинка

Elaine Knuth Trading Between the Lines. Pattern Recognition and Visualization of Markets

3827.16 руб. или Купить в рассрочку!
Insights into a pattern-based method of trading that can increase the likelihood of profitable outcomes While most books on chart patterns, or pattern recognition, offer detailed discussion and analysis of one type of pattern, the fact is that a single pattern may not be very helpful for trading, since it often does not give a complete picture of the market. What sets Trading Between the Lines apart from other books in this area is author Elaine Knuth's identification of sets of patterns that give a complete analysis of the market. In it, she identifies more complex chart patterns, often several patterns combined over multiple time frames, and skillfully examines these sets of patterns called «constellations» in relation to one another. These constellations turn sets of individual patterns into a more manageable set of patterns, where the relationship between them can lead to tactical trading opportunities. Shows how to apply complex patterns to specific trades and identify opportunities as well entry and exit points Markets covered include commodities, equities, and indexes Presents an effective trading approach based on real market cycles-as opposed to computer simulations-that are found in active markets Moving beyond the simple identification of basic patterns to identifying pattern constellations, this reliable resource will give you a better view of what is really going on in the market and help you profit from the opportunities you uncover.
John Shynk J. Probability, Random Variables, and Random Processes. Theory and Signal Processing Applications John Shynk J. Probability, Random Variables, and Random Processes. Theory and Signal Processing Applications Новинка

John Shynk J. Probability, Random Variables, and Random Processes. Theory and Signal Processing Applications

11098.76 руб. или Купить в рассрочку!
Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.
Sabine Landau Cluster Analysis Sabine Landau Cluster Analysis Новинка

Sabine Landau Cluster Analysis

7181.42 руб. или Купить в рассрочку!
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: • Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis. • Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies • Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data. Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics Новинка

Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics

20207.4 руб. или Купить в рассрочку!
Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.
Bjarne Toft Graph Edge Coloring. Vizing's Theorem and Goldberg's Conjecture Bjarne Toft Graph Edge Coloring. Vizing's Theorem and Goldberg's Conjecture Новинка

Bjarne Toft Graph Edge Coloring. Vizing's Theorem and Goldberg's Conjecture

8769.33 руб. или Купить в рассрочку!
Features recent advances and new applications in graph edge coloring Reviewing recent advances in the Edge Coloring Problem, Graph Edge Coloring: Vizing's Theorem and Goldberg's Conjecture provides an overview of the current state of the science, explaining the interconnections among the results obtained from important graph theory studies. The authors introduce many new improved proofs of known results to identify and point to possible solutions for open problems in edge coloring. The book begins with an introduction to graph theory and the concept of edge coloring. Subsequent chapters explore important topics such as: Use of Tashkinov trees to obtain an asymptotic positive solution to Goldberg's conjecture Application of Vizing fans to obtain both known and new results Kierstead paths as an alternative to Vizing fans Classification problem of simple graphs Generalized edge coloring in which a color may appear more than once at a vertex This book also features first-time English translations of two groundbreaking papers written by Vadim Vizing on an estimate of the chromatic class of a p-graph and the critical graphs within a given chromatic class. Written by leading experts who have reinvigorated research in the field, Graph Edge Coloring is an excellent book for mathematics, optimization, and computer science courses at the graduate level. The book also serves as a valuable reference for researchers interested in discrete mathematics, graph theory, operations research, theoretical computer science, and combinatorial optimization.
Duncan Irving H.B. Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models Duncan Irving H.B. Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models Новинка

Duncan Irving H.B. Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

6059.67 руб. или Купить в рассрочку!
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.
Chunlei Tang The Data Industry. The Business and Economics of Information and Big Data Chunlei Tang The Data Industry. The Business and Economics of Information and Big Data Новинка

Chunlei Tang The Data Industry. The Business and Economics of Information and Big Data

6043.21 руб. или Купить в рассрочку!
Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
Mark Grand Patterns in Java. A Catalog of Reusable Design Patterns Illustrated with UML Mark Grand Patterns in Java. A Catalog of Reusable Design Patterns Illustrated with UML Новинка

Mark Grand Patterns in Java. A Catalog of Reusable Design Patterns Illustrated with UML

4592.59 руб. или Купить в рассрочку!
"This is the best book on patterns since the Gang of Four's Design Patterns. The book manages to be a resource for three of the most important trends in professional programming: Patterns, Java, and UML." —Larry O'Brien, Founding Editor, Software Development Magazine Since the release of Design Patterns in 1994, patterns have become one of the most important new technologies contributing to software design and development. In this volume Mark Grand presents 41 design patterns that help you create more elegant and reusable designs. He revisits the 23 «Gang of Four» design patterns from the perspective of a Java programmer and introduces many new patterns specifically for Java. Each pattern comes with the complete Java source code and is diagrammed using UML. Patterns in Java, Volume 1 gives you: 11 Behavioral Patterns, 9 Structural Patterns, 7 Concurrency Patterns, 6 Creational Patterns, 5 Fundamental Design Patterns, and 3 Partitioning Patterns Real-world case studies that illustrate when and how to use the patterns Introduction to UML with examples that demonstrate how to express patterns using UML The CD-ROM contains: Java source code for the 41 design patterns Trial versions of Together/J Whiteboard Edition from Object International (www.togetherj.com); Rational Rose 98 from Rational Software (www.rational.com); System Architect from Popkin Software (www.popkin.com); and OptimizeIt from Intuitive Systems, Inc.
Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data Новинка

Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data

14136.21 руб. или Купить в рассрочку!
The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.
Dhammika Amaratunga Exploration and Analysis of DNA Microarray and Other High-Dimensional Data Dhammika Amaratunga Exploration and Analysis of DNA Microarray and Other High-Dimensional Data Новинка

Dhammika Amaratunga Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

Praise for the First Edition “…extremely well written…a comprehensive and up-to-date overview of this important field.” – Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity. The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.
Harvey Goldstein Methodological Developments in Data Linkage Harvey Goldstein Methodological Developments in Data Linkage Новинка

Harvey Goldstein Methodological Developments in Data Linkage

6713.09 руб. или Купить в рассрочку!
A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.
Vijay Kumar Fundamentals of Pervasive Information Management Systems Vijay Kumar Fundamentals of Pervasive Information Management Systems Новинка

Vijay Kumar Fundamentals of Pervasive Information Management Systems

7631.11 руб. или Купить в рассрочку!
A comprehensive new edition on mobile computing—covering both mobile and sensor data The new paradigm of pervasive computing was born from the needs of highly mobile workers to access and transfer data while on the go. Significant advances in the technology have lent and will continue to lend prevalence to its use—especially in m-commerce. Covering both mobile data and sensor data, this comprehensive text offers updated research on sensor technology, data stream processing, mobile database security, and contextual processing. Packed with cases studies, exercises, and examples, Fundamentals of Pervasive Information Management Systems covers essential aspects of wireless communication and provides a thorough discussion about managing information on mobile database systems (MDS). It addresses the integration of web and workflow with mobile computing and looks at the current state of research. Fundamentals of Pervasive Information Management Systems presents chapters on: Mobile Database System Mobile and Wireless Communication Location and Handoff Management Fundamentals of Database Processing Introduction to Concurrency Control Mechanisms Effect of Mobility on Data Processing Transaction Management in Mobile Database Systems Mobile Database Recovery Wireless Information Dissemination Introduction to Sensor Technology Sensor Technology and Data Streams Management Sensor Network Deployment: Case Studies Fundamentals of Pervasive Information Management Systems is an ideal book for researchers, teachers, and graduate students of mobile computing. The book may also be used as a reference text for researchers or managers.
Jerome Beranger The Algorithmic Code of Ethics. Ethics at the Bedside of the Digital Revolution Jerome Beranger The Algorithmic Code of Ethics. Ethics at the Bedside of the Digital Revolution Новинка

Jerome Beranger The Algorithmic Code of Ethics. Ethics at the Bedside of the Digital Revolution

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The technical progress illustrated by the development of Artificial Intelligence (AI), Big Data technologies, the Internet of Things (IoT), online platforms, NBICs, autonomous expert systems, and the Blockchain let appear the possibility of a new world and the emergence of a fourth industrial revolution centered around digital data. Therefore, the advent of digital and its omnipresence in our modern society create a growing need to lay ethical benchmarks against this new religion of data, the «dataisme».
AICPA Guide to Audit Data Analytics AICPA Guide to Audit Data Analytics Новинка

AICPA Guide to Audit Data Analytics

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Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as “the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit.” Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix.
Pushpak Sarkar Data as a Service. A Framework for Providing Reusable Enterprise Data Services Pushpak Sarkar Data as a Service. A Framework for Providing Reusable Enterprise Data Services Новинка

Pushpak Sarkar Data as a Service. A Framework for Providing Reusable Enterprise Data Services

5287.99 руб. или Купить в рассрочку!
Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions
Markus Eichhorn Natural Systems. The Organisation of Life Markus Eichhorn Natural Systems. The Organisation of Life Новинка

Markus Eichhorn Natural Systems. The Organisation of Life

Organised into four sections, this text discusses the organisation of the living world. Links Ecology, Biodiversity and Biogeography Bridges modern and conventional Ecology Builds sequentially from the concept and importance of species, through patterns of diversity to help consider global patterns of biogeography Uses real data sets to help train in essential skills
Timothy Veenstra D. Proteomic Applications in Cancer Detection and Discovery Timothy Veenstra D. Proteomic Applications in Cancer Detection and Discovery Новинка

Timothy Veenstra D. Proteomic Applications in Cancer Detection and Discovery

Helps researchers in proteomics and oncology work together to understand, prevent, and cure cancer Proteomic data is increasingly important to understanding the origin and progression of cancer; however, most oncologic researchers who depend on proteomics for their studies do not collect the data themselves. As a result, there is a knowledge gap between scientists, who devise proteomic techniques and collect the data, and the oncologic researchers, who are expected to interpret and apply proteomic data. Bridging the gap between proteomics and oncology research, this book explains how proteomic technology can be used to address some of the most important questions in cancer research. Proteomic Applications in Cancer Detection and Discovery enables readers to understand how proteomic data is acquired and analyzed and how it is interpreted. Author Timothy Veenstra has filled the book with examples—many based on his own firsthand research experience—that clearly demonstrate the application of proteomic technology in oncology research, including the discovery of novel biomarkers for different types of cancers. The book begins with a brief introduction to systems biology, explaining why cancer is a systems biology disease. Next, it covers such topics as: Mass spectrometry in cancer research Application of proteomics to global phosphorylation analysis Search for biomarkers in biofluids Rise and fall of proteomic patterns for cancer diagnostics Emergence of protein arrays Role of proteomics in personalized medicine The final chapter is dedicated to the future prospects of proteomics in cancer research. By guiding readers through the latest proteomic technologies and their applications in cancer research, Proteomic Applications in Cancer Detection and Discovery enhances the ability of researchers in proteomics and researchers in oncology to collaborate in order to better understand cancer and develop strategies to prevent and treat it.
Michael Kircher Pattern-Oriented Software Architecture, Patterns for Resource Management Michael Kircher Pattern-Oriented Software Architecture, Patterns for Resource Management Новинка

Michael Kircher Pattern-Oriented Software Architecture, Patterns for Resource Management

5660.37 руб. или Купить в рассрочку!
The first volume of the POSA pattern series introduced a broad-spectrum of general-purpose patterns in software design and architecture. The second narrowed the focus to fundamental patterns for building sophisticated concurrent and networked software systems and applications. This volume uses design patterns to present techniques for implementing effective resource management in a system. The patterns are covered in detail making use of several examples providing directions to the readers on how to implement the presented patterns. Additionally, the volume presents a thorough introduction into resource management and a case study where the patterns are applied to the domain of mobile radio networks. The patterns are grouped by different areas of resource management and hence address the complete lifecycle of resources: resource acquisition, coordination and release.
Qihao Weng Scale Issues in Remote Sensing Qihao Weng Scale Issues in Remote Sensing Новинка

Qihao Weng Scale Issues in Remote Sensing

10716.05 руб. или Купить в рассрочку!
Provides up-to-date developments in the field of remote sensing by assessing scale issues in land surface, properties, patterns, and processes Scale is a fundamental and crucial issue in remote sensing studies and image analysis. GIS and remote sensing scientists use various scaling techniques depending on the types of remotely sensed images and geospatial data used. Scaling techniques affect image analysis such as object identification and change detection. This book offers up-to-date developments, methods, and techniques in the field of GIS and remote sensing and features articles from internationally renowned authorities on three interrelated perspectives of scaling issues: scale in land surface properties, land surface patterns, and land surface processes. It also visits and reexamines the fundamental theories of scale and scaling by well-known experts who have done substantial research on the topics. Edited by a prominent authority in the geographic information science community, Scale Issues in Remote Sensing: Offers an extensive examination of the fundamental theories of scale issues along with current scaling techniques Studies scale issues from three interrelated perspectives: land surface properties, patterns, and processes Addresses the impact of new frontiers in Earth observation technology (high-resolution, hyperspectral, Lidar sensing, and their synergy with existing technologies) and advances in remote sensing imaging science (object-oriented image analysis and data fusion) Prospects emerging and future trends in remote sensing and their relationship with scale Scale Issues in Remote Sensing is ideal as a professional reference for practicing geographic information scientists and remote sensing engineers as well as supplemental reading for graduate level students.
Patrick Muldowney A Modern Theory of Random Variation. With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration Patrick Muldowney A Modern Theory of Random Variation. With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration Новинка

Patrick Muldowney A Modern Theory of Random Variation. With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration

9720.99 руб. или Купить в рассрочку!
A ground-breaking and practical treatment of probability and stochastic processes A Modern Theory of Random Variation is a new and radical re-formulation of the mathematical underpinnings of subjects as diverse as investment, communication engineering, and quantum mechanics. Setting aside the classical theory of probability measure spaces, the book utilizes a mathematically rigorous version of the theory of random variation that bases itself exclusively on finitely additive probability distribution functions. In place of twentieth century Lebesgue integration and measure theory, the author uses the simpler concept of Riemann sums, and the non-absolute Riemann-type integration of Henstock. Readers are supplied with an accessible approach to standard elements of probability theory such as the central limmit theorem and Brownian motion as well as remarkable, new results on Feynman diagrams and stochastic integrals. Throughout the book, detailed numerical demonstrations accompany the discussions of abstract mathematical theory, from the simplest elements of the subject to the most complex. In addition, an array of numerical examples and vivid illustrations showcase how the presented methods and applications can be undertaken at various levels of complexity. A Modern Theory of Random Variation is a suitable book for courses on mathematical analysis, probability theory, and mathematical finance at the upper-undergraduate and graduate levels. The book is also an indispensible resource for researchers and practitioners who are seeking new concepts, techniques and methodologies in data analysis, numerical calculation, and financial asset valuation. Patrick Muldowney, PhD, served as lecturer at the Magee Business School of the UNiversity of Ulster for over twenty years. Dr. Muldowney has published extensively in his areas of research, including integration theory, financial mathematics, and random variation.
Guttman Irwin Statistics and Probability with Applications for Engineers and Scientists Guttman Irwin Statistics and Probability with Applications for Engineers and Scientists Новинка

Guttman Irwin Statistics and Probability with Applications for Engineers and Scientists

11098.76 руб. или Купить в рассрочку!
Introducing the tools of statistics and probability from the ground up An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences. Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.
Mark Grand Java Enterprise Design Patterns. Patterns in Java Mark Grand Java Enterprise Design Patterns. Patterns in Java Новинка

Mark Grand Java Enterprise Design Patterns. Patterns in Java

3188.66 руб. или Купить в рассрочку!
A how-to guide for Java programmers who want to use design patterns when developing real-world enterprise applications This practical book explores the subject of design patterns, or patterns that occur in the design phase of a project's life cycle. With an emphasis on Java for the enterprise, Mark Grand guides Java programmers on how to apply traditional and new patterns when designing a large enterprise application. The author clearly explains how existing patterns work with the new enterprise design patterns and demonstrates through case studies how to use design patterns in the real world. Features include over 50 design patterns, each mapped out by UML, plus an overview of UML 1.4 and how it fits in with the different phases of a project's life cycle.
Laurence Yang T. Mobile Intelligence Laurence Yang T. Mobile Intelligence Новинка

Laurence Yang T. Mobile Intelligence

13777.78 руб. или Купить в рассрочку!
* Focuses on learning patterns and knowledge from data generated by mobile users and mobile technology. * Covers research and application issues in applying computational intelligence applications to mobile computing * Delivers benefits to a wide range of applications * Introduces the state of the art of computational intelligence to the mobile paradigm
Dean Karnopp C. System Dynamics. Modeling, Simulation, and Control of Mechatronic Systems Dean Karnopp C. System Dynamics. Modeling, Simulation, and Control of Mechatronic Systems Новинка

Dean Karnopp C. System Dynamics. Modeling, Simulation, and Control of Mechatronic Systems

12095.75 руб. или Купить в рассрочку!
An expanded new edition of the bestselling system dynamics book using the bond graph approach A major revision of the go-to resource for engineers facing the increasingly complex job of dynamic systems design, System Dynamics, Fifth Edition adds a completely new section on the control of mechatronic systems, while revising and clarifying material on modeling and computer simulation for a wide variety of physical systems. This new edition continues to offer comprehensive, up-to-date coverage of bond graphs, using these important design tools to help readers better understand the various components of dynamic systems. Covering all topics from the ground up, the book provides step-by-step guidance on how to leverage the power of bond graphs to model the flow of information and energy in all types of engineering systems. It begins with simple bond graph models of mechanical, electrical, and hydraulic systems, then goes on to explain in detail how to model more complex systems using computer simulations. Readers will find: New material and practical advice on the design of control systems using mathematical models New chapters on methods that go beyond predicting system behavior, including automatic control, observers, parameter studies for system design, and concept testing Coverage of electromechanical transducers and mechanical systems in plane motion Formulas for computing hydraulic compliances and modeling acoustic systems A discussion of state-of-the-art simulation tools such as MATLAB and bond graph software Complete with numerous figures and examples, System Dynamics, Fifth Edition is a must-have resource for anyone designing systems and components in the automotive, aerospace, and defense industries. It is also an excellent hands-on guide on the latest bond graph methods for readers unfamiliar with physical system modeling.
Olivier Pivert NoSQL Data Models. Trends and Challenges Olivier Pivert NoSQL Data Models. Trends and Challenges Новинка

Olivier Pivert NoSQL Data Models. Trends and Challenges

9248.97 руб. или Купить в рассрочку!
The topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. The chapters in this book report on research about the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues.
Chong Tow C. Developments in Data Storage. Materials Perspective Chong Tow C. Developments in Data Storage. Materials Perspective Новинка

Chong Tow C. Developments in Data Storage. Materials Perspective

10486.42 руб. или Купить в рассрочку!
A timely text on the recent developments in data storage, from a materials perspective Ever-increasing amounts of data storage on hard disk have been made possible largely due to the immense technological advances in the field of data storage materials. Developments in Data Storage: Materials Perspective covers the recent progress and developments in recording technologies, including the emerging non-volatile memory, which could potentially become storage technologies of the future. Featuring contributions from experts around the globe, this book provides engineers and graduate students in materials science and electrical engineering a solid foundation for grasping the subject. The book begins with the basics of magnetism and recording technology, setting the stage for the following chapters on existing methods and related research topics. These chapters focus on perpendicular recording media to underscore the current trend of hard disk media; read sensors, with descriptions of their fundamental principles and challenges; and write head, which addresses the advanced concepts for writing data in magnetic recording. Two chapters are devoted to the highly challenging area in hard disk drives of tribology, which deals with reliability, corrosion, and wear-resistance of the head and media. Next, the book provides an overview of the emerging technologies, such as heat-assisted magnetic recording and bit-patterned media recording. Non-volatile memory has emerged as a promising alternative storage option for certain device applications; two chapters are dedicated to non-volatile memory technologies such as the phase-change and the magnetic random access memories. With a strong focus on the fundamentals along with an overview of research topics, Developments in Data Storage is an ideal reference for graduate students or beginners in the field of magnetic recording. It also serves as an invaluable reference for future storage technologies including non-volatile memories.
Brown Helen Dawes Applied Mixed Models in Medicine Brown Helen Dawes Applied Mixed Models in Medicine Новинка

Brown Helen Dawes Applied Mixed Models in Medicine

7862.06 руб. или Купить в рассрочку!
A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott’s groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.
Anne Bouillard Deterministic Network Calculus. From Theory to Practical Implementation Anne Bouillard Deterministic Network Calculus. From Theory to Practical Implementation Новинка

Anne Bouillard Deterministic Network Calculus. From Theory to Practical Implementation

15155.55 руб. или Купить в рассрочку!
Deterministic network calculus is a theory based on the (min,plus) algebra. Its aim is to compute worst-case performance bounds in communication networks. Our goal is to provide a comprehensive view of this theory and its recent advances, from its theoretical foundations to its implementations. The book is divided into three parts. The first part focuses on the (min,plus) framework and its algorithmic aspects. The second part defines the network calculus model and analyzes one server in isolation. Different service and scheduling policies are discussed, particularly when data is packetized. The third part is about network analyses. Pay burst only once and pay multiplexing only once phenomena are exhibited, and different analyses are proposed and compared. This includes the linear programming approaches that compute tight performance bounds. Finally, some partial results on the stability are detailed.
Xiaoyu Cai Formation Control of Multi-Agent Systems. A Graph Rigidity Approach Xiaoyu Cai Formation Control of Multi-Agent Systems. A Graph Rigidity Approach Новинка

Xiaoyu Cai Formation Control of Multi-Agent Systems. A Graph Rigidity Approach

12859.26 руб. или Купить в рассрочку!
Formation Control of Multi-Agent Systems: A Graph Rigidity Approach Marcio de Queiroz, Louisiana State University, USA Xiaoyu Cai, FARO Technologies, USA Matthew Feemster, U.S. Naval Academy, USA A comprehensive guide to formation control of multi-agent systems using rigid graph theory This book is the first to provide a comprehensive and unified treatment of the subject of graph rigidity-based formation control of multi-agent systems. Such systems are relevant to a variety of emerging engineering applications, including unmanned robotic vehicles and mobile sensor networks. Graph theory, and rigid graphs in particular, provides a natural tool for describing the multi-agent formation shape as well as the inter-agent sensing, communication, and control topology. Beginning with an introduction to rigid graph theory, the contents of the book are organized by the agent dynamic model (single integrator, double integrator, and mechanical dynamics) and by the type of formation problem (formation acquisition, formation manoeuvring, and target interception). The book presents the material in ascending level of difficulty and in a self-contained manner; thus, facilitating reader understanding. Key features: Uses the concept of graph rigidity as the basis for describing the multi-agent formation geometry and solving formation control problems. Considers different agent models and formation control problems. Control designs throughout the book progressively build upon each other. Provides a primer on rigid graph theory. Combines theory, computer simulations, and experimental results. Formation Control of Multi-Agent Systems: A Graph Rigidity Approach is targeted at researchers and graduate students in the areas of control systems and robotics. Prerequisite knowledge includes linear algebra, matrix theory, control systems, and nonlinear systems.
Bart Baesens Analytics in a Big Data World. The Essential Guide to Data Science and its Applications Bart Baesens Analytics in a Big Data World. The Essential Guide to Data Science and its Applications Новинка

Bart Baesens Analytics in a Big Data World. The Essential Guide to Data Science and its Applications

3186.11 руб. или Купить в рассрочку!
The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.
Neil Jacobsen E. NMR Data Interpretation Explained. Understanding 1D and 2D NMR Spectra of Organic Compounds and Natural Products Neil Jacobsen E. NMR Data Interpretation Explained. Understanding 1D and 2D NMR Spectra of Organic Compounds and Natural Products Новинка

Neil Jacobsen E. NMR Data Interpretation Explained. Understanding 1D and 2D NMR Spectra of Organic Compounds and Natural Products

11339.09 руб. или Купить в рассрочку!
Through numerous examples, the principles of the relationship between chemical structure and the NMR spectrum are developed in a logical, step-by-step fashion Includes examples and exercises based on real NMR data including full 600 MHz one- and two-dimensional datasets of sugars, peptides, steroids and natural products Includes detailed solutions and explanations in the text for the numerous examples and problems and also provides large, very detailed and annotated sets of NMR data for use in understanding the material Describes both simple aspects of solution-state NMR of small molecules as well as more complex topics not usually covered in NMR books such as complex splitting patterns, weak long-range couplings, spreadsheet analysis of strong coupling patterns and resonance structure analysis for prediction of chemical shifts Advanced topics include all of the common two-dimensional experiments (COSY, ROESY, NOESY, TOCSY, HSQC, HMBC) covered strictly from the point of view of data interpretation, along with tips for parameter settings
Thomas Hammergren C. Data Warehousing For Dummies Thomas Hammergren C. Data Warehousing For Dummies Новинка

Thomas Hammergren C. Data Warehousing For Dummies

2231.87 руб. или Купить в рассрочку!
Data warehousing is one of the hottest business topics, and there’s more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition. Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to: Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic!
Stéphane Tufféry Data Mining and Statistics for Decision Making Stéphane Tufféry Data Mining and Statistics for Decision Making Новинка

Stéphane Tufféry Data Mining and Statistics for Decision Making

7880.12 руб. или Купить в рассрочку!
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
Majid Jaberi-Douraki Mathematical Modelling. A Graduate Textbook Majid Jaberi-Douraki Mathematical Modelling. A Graduate Textbook Новинка

Majid Jaberi-Douraki Mathematical Modelling. A Graduate Textbook

7616.77 руб. или Купить в рассрочку!
An important resource that provides an overview of mathematical modelling Mathematical Modelling offers a comprehensive guide to both analytical and computational aspects of mathematical modelling that encompasses a wide range of subjects. The authors provide an overview of the basic concepts of mathematical modelling and review the relevant topics from differential equations and linear algebra. The text explores the various types of mathematical models, and includes a range of examples that help to describe a variety of techniques from dynamical systems theory. The book’s analytical techniques examine compartmental modelling, stability, bifurcation, discretization, and fixed-point analysis. The theoretical analyses involve systems of ordinary differential equations for deterministic models. The text also contains information on concepts of probability and random variables as the requirements of stochastic processes. In addition, the authors describe algorithms for computer simulation of both deterministic and stochastic models, and review a number of well-known models that illustrate their application in different fields of study. This important resource: Includes a broad spectrum of models that fall under deterministic and stochastic classes and discusses them in both continuous and discrete forms Demonstrates the wide spectrum of problems that can be addressed through mathematical modelling based on fundamental tools and techniques in applied mathematics and statistics Contains an appendix that reveals the overall approach that can be taken to solve exercises in different chapters Offers many exercises to help better understand the modelling process Written for graduate students in applied mathematics, instructors, and professionals using mathematical modelling for research and training purposes, Mathematical Modelling: A Graduate Textbook covers a broad range of analytical and computational aspects of mathematical modelling.
Tony Boobier Analytics for Insurance. The Real Business of Big Data Tony Boobier Analytics for Insurance. The Real Business of Big Data Новинка

Tony Boobier Analytics for Insurance. The Real Business of Big Data

The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.
Nathan Yau Data Points. Visualization That Means Something Nathan Yau Data Points. Visualization That Means Something Новинка

Nathan Yau Data Points. Visualization That Means Something

A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribers Focuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and more Examines standard rules across all visualization applications, then explores when and where you can break those rules Create visualizations that register at all levels, with Data Points: Visualization That Means Something.
Alan Agresti Categorical Data Analysis Alan Agresti Categorical Data Analysis Новинка

Alan Agresti Categorical Data Analysis

11481.48 руб. или Купить в рассрочку!
Praise for the Second Edition «A must-have book for anyone expecting to do research and/or applications in categorical data analysis.» —Statistics in Medicine «It is a total delight reading this book.» —Pharmaceutical Research «If you do any analysis of categorical data, this is an essential desktop reference.» —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Frank Ohlhorst J. Big Data Analytics. Turning Big Data into Big Money Frank Ohlhorst J. Big Data Analytics. Turning Big Data into Big Money Новинка

Frank Ohlhorst J. Big Data Analytics. Turning Big Data into Big Money

3186.11 руб. или Купить в рассрочку!
Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

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Presents an important and unique introduction to random walk theory Random walk is a stochastic process that has proven to be a useful model in understanding discrete-state discrete-time processes across a wide spectrum of scientific disciplines. Elements of Random Walk and Diffusion Processes provides an interdisciplinary approach by including numerous practical examples and exercises with real-world applications in operations research, economics, engineering, and physics. Featuring an introduction to powerful and general techniques that are used in the application of physical and dynamic processes, the book presents the connections between diffusion equations and random motion. Standard methods and applications of Brownian motion are addressed in addition to Levy motion, which has become popular in random searches in a variety of fields. The book also covers fractional calculus and introduces percolation theory and its relationship to diffusion processes. With a strong emphasis on the relationship between random walk theory and diffusion processes, Elements of Random Walk and Diffusion Processes features: Basic concepts in probability, an overview of stochastic and fractional processes, and elements of graph theory Numerous practical applications of random walk across various disciplines, including how to model stock prices and gambling, describe the statistical properties of genetic drift, and simplify the random movement of molecules in liquids and gases Examples of the real-world applicability of random walk such as node movement and node failure in wireless networking, the size of the Web in computer science, and polymers in physics Plentiful examples and exercises throughout that illustrate the solution of many practical problems Elements of Random Walk and Diffusion Processes is an ideal reference for researchers and professionals involved in operations research, economics, engineering, mathematics, and physics. The book is also an excellent textbook for upper-undergraduate and graduate level courses in probability and stochastic processes, stochastic models, random motion and Brownian theory, random walk theory, and diffusion process techniques.
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