Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

Based on the ontology and semantics of algebra, the computer algebra system Magma enables users to rapidly formulate and perform calculations in abstract parts of mathematics. Edited by the principal designers of the program, this book explores Magma. Coverage ranges from number theory and algebraic geometry, through representation theory and group theory to discrete mathematics and graph theory. Includes case studies describing computations underpinning new theoretical results.

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them. See Professor Hilbe discuss the book.

In many fields of modern mathematics specialised scientific software becomes increasingly important. Hence, tremendous effort is taken by numerous groups all over the world to develop appropriate solutions.
This book contains surveys and research papers on mathematical software and algorithms. The common thread is that the field of mathematical applications lies on the border between algebra and geometry. Topics include polyhedral geometry, elimination theory, algebraic surfaces, Grobner bases, triangulations of point sets and the mutual relationship. This diversity is accompanied by the abundance of available software systems which often handle only special mathematical aspects. Therefore the volume's other focus is on solutions towards the integration of mathematical software systems. This includes low-level and XML based high-level communication channels as well as general framework for modular systems."

Applied statisticians often need to perform analyses of multivariate data; for these they will typically use one of the statistical software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save users having to buy two books for the job. The author is well-known for this kind of book, and so buyers will trust that he 's got it right.

Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the "average" patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model. In medicine there are more and more attempts to individualize therapy; thus, from the author's point of view biostatis- cians should support these efforts. Therefore, one of the tasks of the statistician is to identify heterogeneity of patients and, if possible, to explain part of it with known explanatory covariates. Finite mixture models may be used to aid this purpose. This book tries to show that there are a large range of applications. They include the analysis of gene - pression data, pharmacokinetics, toxicology, and the determinants of beta-carotene plasma levels. Other examples include disease clustering, data from psychophysi- ogy, and meta-analysis of published studies. The book is intended as a resource for those interested in applying these methods.

While theoretical statistics relies primarily on mathematics and hypothetical situations, statistical practice is a translation of a question formulated by a researcher into a series of variables linked by a statistical tool. As with written material, there are almost always differences between the meaning of the original text and translated text. Additionally, many versions can be suggested, each with their advantages and disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations. As indicated in the title, the syntax of these statistical formulations is based on the well-known R language, chosen for its popularity, simplicity, and power of its structure. Although syntax is vital, understanding the semantics is the real challenge of any good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand. Sometimes data sets contain errors, inconsistencies between answers, or missing data. More often, available statistical tools are not formally appropriate for the given situation, making it difficult to assess to what extent this slight inadequacy affects the interpretation of results. Analysis of Questionnaire Data with R tackles these and other common challenges in the practice of statistics.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.

The contributions in this book state the complementary rather than competitive relationship between Probability and Fuzzy Set Theory and allow solutions to real life problems with suitable combinations of both theories.

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.

SharePoint is gaining recognition as a full-fledged application server with many features and enhancements that specifically allow non-developers to create sophisticated intranet sites. However, with the 2010 release, Microsoft's SharePoint increasingly becomes a compelling development platform. The strong application programming interface (API), its highly extensible nature, and its foundation on the underlying .NET Framework all generate the perfect storm to make it one of the most powerful web development platforms available.

However, with power comes complexity. The wide range of usage scenarios make it difficult for developers to grasp the full ability of this next-generation platform. This book takes an in-depth, all-encompassing approach to programming concepts, the extensibility interfaces, and how to embrace SharePoint as a toolkit full of features available to web developers.Take an in-depth look into the internals of SharePoint. Create sophisticated applications using SharePoint controls and databases. Understand the API and use in conjunction with ASP.NET to extend SharePoint.

SharePoint is more than a portal and more than an intranet. Harness its capabilities and put it to work for you. What you'll learn The hierarchy of SharePoint's API How to create rich, extensible, and broad SharePoint applications How to use SharePoint's internals How to approach SharePoint as an open toolkit rather than a closed, intranet-only model How to take advantage of SharePoint's extensibility and customize its behavior Who this book is for

This book is for ASP.NET developers who want to create applications using SharePoint as a platform. It's also for users of SharePoint Designer that want to professionalize their development work. Table of Contents Developer Basics Architecture Accessing the API Data Access External Data Access Web Parts Templates Application Techniques Solution Deployment Extending the User Interface Using Web Controls Client Programming Integrating Silverlight Integrating Charts and Maps Forms Services and InfoPath Workflows Administrative Tasks Enterprise Features

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.

Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.

The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.

This book provides a quick access to computational tools for algebraic geometry, the mathematical discipline which handles solution sets of polynomial equations. Originating from a number of intense one week schools taught by the authors, the text is designed so as to provide a step by step introduction which enables the reader to get started with his own computational experiments right away. The authors present the basic concepts and ideas in a compact way.

Linear Algebra: An Introduction With Mathematica uses a matrix-based presentation and covers the standard topics any mathematician will need to understand linear algebra while using Mathematica. Development of analytical and computational skills is emphasized, and worked examples provide step-by-step methods for solving basic problems using Mathematica. The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics.

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do simulations and experiments. R can be useful for student projects, for creating graphics for teaching, as well as for scholarly work. This book presents ways R, which is freely available, can enhance the teaching of mathematics and statistics. R has the potential to help students learn mathematics due to the need for precision, understanding of symbols and functions, and the logical nature of code. Moreover, the text provides students the opportunity for experimenting with concepts in any mathematics course. Features: Does not require previous experience with R Promotes the use of R in typical mathematics and statistics course work Organized by mathematics topics Utilizes an example-based approach Chapters are largely independent of each other

In the history of mathematics there are many situations in which cal- lations were performed incorrectly for important practical applications. Let us look at some examples, the history of computing the number ? began in Egypt and Babylon about 2000 years BC, since then many mathematicians have calculated ? (e. g. , Archimedes, Ptolemy, Vi` ete, etc. ). The ?rst formula for computing decimal digits of ? was disc- ered by J. Machin (in 1706), who was the ?rst to correctly compute 100 digits of ?. Then many people used his method, e. g. , W. Shanks calculated ? with 707 digits (within 15 years), although due to mistakes only the ?rst 527 were correct. For the next examples, we can mention the history of computing the ?ne-structure constant ? (that was ?rst discovered by A. Sommerfeld), and the mathematical tables, exact - lutions, and formulas, published in many mathematical textbooks, were not veri?ed rigorously [25]. These errors could have a large e?ect on results obtained by engineers. But sometimes, the solution of such problems required such techn- ogy that was not available at that time. In modern mathematics there exist computers that can perform various mathematical operations for which humans are incapable. Therefore the computers can be used to verify the results obtained by humans, to discovery new results, to - provetheresultsthatahumancanobtainwithoutanytechnology. With respectto our example of computing?, we can mention that recently (in 2002) Y. Kanada, Y. Ushiro, H. Kuroda, and M.

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.

The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.

No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

The SharePoint user experience is critical in application architecture and user acceptance. Using tools available to all developers, Building the SharePoint User Experience will show you how to rebuild a SharePoint site, taking it all the way from the default outofthebox experience to your very own customized user experience.

Along the way you will receive a solid understanding of the SharePoint architecture that will enable you to take full advantage of the capabilities of SharePoint as a platform. This will allow you to tailor the SharePoint user experience to increase the value of solutions and to work more effectively with projects. And that, of course, leads to successful SharePoint solutions in your business that your users are happy to accept and use. What you'll learn Learn the tools needed to create effective and highly tailored user interfaces and experiences. Dissect an outofthebox site to learn how every user experience element is built. Master the core functions of sites, lists, content types, and fields. Build a site from scratch using tools available to everyone. Speed up development time by using little known tips and tricks. See how new skills can be applied to everyday tasks with simple exercises. Who this book is for

This book is for SharePoint developers who want to learn how to work with designers and other developers to create custom and tailored user experiences, including custom forms, content types, lists, fields, pages, and navigation that will better match your project's requirements. This is not a book for graphic designers--you'll find no Photoshop tips here Table of Contents Checking Your Gear for Departure Taking a Crash Course in XML Exploring Feature Basics and Not-So Basics Excavating the Site Evolving the Default User Experience What Lurks in the Forest of Lists? Encountering the Monsters in the Cave The Liquid Nitrogen of SharePoint Strolling Through Fields of Gold Intermission: The Mentality of a SharePoint Developer Starting Field Creating Your First Content Factory Accounting Gone Haywire Pages and Pages of Fun Our Empire United

Resistance to Anti-CD20 Antibodies and Approaches for Their Reversal presents in-depth content written by international experts in the study of resistance to anti-CD20 antibodies and approaches for their reversal. Anti-CD20 antibodies are used to achieve B cell depletion and are developed to treat B cell proliferative disorders, including non-Hodgkin’s lymphoma and chronic lymphocytic leukemia. In the past two decades, anti-CD20 antibodies have revolutionized the treatment of all B cell malignancies, however, there are patients that fail to respond to initial therapy or relapse sooner. This book explores new and existing avenues surrounding Anti-CD20 antibodies. In recent years, several next-generation anti-CD20 therapies have been developed but predicting and reversing resistance is still a challenging task. These areas are being actively studied as they represent a potential to improve anti-CD20 therapies and are discussed thoroughly in the book. It is a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about resistance to anti-CD20 antibodies and their reversal.

Success with Microsoft Dynamics CRM 4.0: Implementing Customer Relationship Management is aimed at readers who are interested in understanding how to successfully implement Microsoft Dynamics CRM 4.0 within their projects. It is intended as an implementation roadmap for the business and technical representatives leading or engaged in a project.

The book covers the capabilities of Microsoft Dynamics CRM, both in the traditional functional areas of sales, marketing, and service and as an applications framework for XRM deployments. The book demonstrates CRM best practices for design, configuration, and development. Through realworld solutions and exercises, you will be given the confidence and expertise to deliver an implementation that provides longterm success for your organization.

Everyone has an idea that they think is the next big thing. The problem is, it's probably an app or software idea and most people probably don't know how to code and their record for managing programmers is little to none. Even if they do know how to code, they're not quite sure how to get their first one thousand customers. The Non-Technical Founder walks readers through the stages of validating whether their next big thing is good, bringing the idea to life, and getting those first customers.

Dem gestiegenen Wettbewerbsdruck begegnen die Unternehmen neben Rationalisierungs- und Differenzierungsmassnahmen vor allem durch Kundenbindung. Eine zentrale Rolle spielt hierbei die Ausweitung und kundenindividuelle Gestaltung von Dienstleistungsangeboten. Die modellbasierte Entwicklung und kontinuierliche Verbesserung von Dienstleistungen gewinnen daher immer mehr an Bedeutung. Der Band gibt einen Uberblick uber die hierbei einsetzbaren Methoden und zeigt zukunftige Entwicklungsperspektiven auf. Die zahlreichen Beispiele, die unterschiedlichen Branchen entstammen, fokussieren einerseits Modelle zur Dienstleistungsentwicklung und -erbringung und andererseits Modelle von Informationssystemen, welche die Entwicklung und/oder die Erbringung von Dienstleistungen unterstutzen. Sie betrachten alle Lebenszyklusphasen von Dienstleistungen sowie alle Dimensionen des Dienstleistungsbegriffs, die eine Basis zur Entwicklung von Ressourcen-, Prozess- und Produktmodellen darstellen."