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."

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.

Focusing on the R software, the first section covers:

• Basic elements of the R software and data processing
• Clear, concise visualization of results, using simple and complex graphs
• Programming basics: pre-defined and user-created functions

The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:

• Regression methods
• Analyses of variance and covariance
• Classification methods
• Exploratory multivariate analysis
• Clustering methods
• Hypothesis tests

After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

Datasets and all the results described in this book are available on the book s webpage at http: //www.agrocampus-ouest.fr/math/RforStat

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.

The first edition was released in 1996 and has sold close to 2200 copies.

Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space.

The authors have added three chapters and exercise sets. The text is being moved from SSS to SSPP.

The book is suitable for courses in statistics for the social or managerial sciences as well as for advanced courses on MDS.

All the mathematics required for more advanced topics is developed systematically in the text.

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.

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.

Recent advances in the understanding of star formation and evolution have been impressive and aspects of that knowledge are explored in this volume. The black hole stellar endpoints are studied and geodesic motion is explored. The emission of gravitational waves is featured due to their very recent experimental discovery.The second aspect of the text is space exploration which began 62 years ago with the Sputnik Earth satellite followed by the landing on the Moon just 50 years ago. Since then Mars has been explored remotely as well as flybys of the outer planets and probes which have escaped the solar system. The text explores many aspects of rocket travel. Finally possibilities for interstellar travel are discussed.All these topics are treated in a unified way using the Matlab App to combine text, figures, formulae and numeric input and output. In this way the reader may vary parameters and see the results in real time. That experience aids in building up an intuitive feel for the many specific problems given in this text.

PowerPivot comprises a set of technologies for easy access to data mining and business intelligence analysis from Microsoft Excel and SharePoint. Power users and developers alike can create sophisticated, online analytic processing (OLAP) solutions using PowerPivot for Excel, and then share those solutions with other users via PowerPivot for SharePoint. Data can be pulled in from any of the leading database platforms, as well as from spreadsheets and flat files PowerPivot for Business Intelligence Using Excel and SharePointis your key to mastering PowerPivot. The book takes a scenario-based approach to showing you how to collect data, to mine that data through insightful analysis, and to draw conclusions that drive business performance. Each chapter in the book is focused on a specific challenge that you'll encounter when using PowerPivot. Each chapter takes you through a solution technique that's been proven in the real world. Covers the leading technology for bringing data analytics to the desktop Presents real-world solutions to real-world scenarios Written by a Microsoft Virtual Technical Specialist (VTS) for business intelligence What you'll learn Install and verify the PowerPivot software Integrated existing, available data to deliver business intelligence Leverage Time Intelligence to report change over time Write Data Analysis Expressions (DAX) to create custom measures Identify and implement solutions for role-playing dimensions Recognize and work-around PowerPivot's missing features Who this book is for

PowerPivot Solutions for Excel and SharePoint is aimed at information workers and data analysts who typically use Excel to drive business decisions. The book shows how you can apply PowerPivot to problems typically addressed through complicated and arcane spreadsheet techniques. Business people without the time and interest in learning Excel arcane will especially appreciate how PowerPivot enables them to easily create models and perform analysis far in advance of anything they could do using Excel alone. Table of Contents Getting Started with PowerPivot for Excel Hello World, PowerPivot Style Combining Data Sources Data Analysis Expressions A Method to the Madness Installing PowerPivot for SharePoint Collaboration, Version Control, and Management PowerPivot As a Data Source PowerPivot and SQL Server Reporting Services PowerPivot and Predictive Analytics Tips, Tricks, and Traps

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.

Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

This book reviews some of today's more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors - largely from Montreal's GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes - present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.

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.

Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.

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.

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

Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling.

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.

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 presents a rigorous mathematical development of soil water and contaminant flow in variably saturated and saturated soils. Analytical and numerical methods are balanced: computer programs, among them MathCad and Fortran, are presented, and more than 150 practice and discussion questions are included. Students are thus exposed not only to theory but also to an array of solutions techniques. Those using the book as a reference will appreciate the careful development of basic flow equations, the inclusion of solutions and methodology currently available only in journals and proceedings volumes, and the examples and calculations directly applicable to their own work.

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

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 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.

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.