This book can be cut, drawn in, folded into shapes and will even take you to the fourth dimension. So join stand-up mathematician Matt Parker on a journey through narcissistic numbers, optimal dating algorithms, at least two different kinds of infinity and more.

    Financial Modeling bridgesthis gap between theory and practice by providing a nuts-and-bolts guide to solvingcommon financial models with spreadsheets. Simon Benninga takes the reader step bystep through each model, showing how it can be solved using Microsoft Excel. Thelong-awaited third edition of this standard text maintains the "cookbook"features and Excel dependence that have made the first and second editions sopopular. It also offers significant new material, with new chapters covering suchtopics as bank valuation, the Black-Litterman approach to portfolio optimization, Monte Carlo methods and their applications to option pricing, and using arrayfunctions and formulas. Other chapters, including those on basic financialcalculations, portfolio models, calculating the variance-covariance matrix, andgenerating random numbers, have been revised, with many offering substantially newand improved material. Other areas covered include financial statement modeling, leasing, standard portfolio problems, value at risk (VaR), real options, durationand immunization, and term structure modeling. Technical chapters treat such topicsas data tables, matrices, the Gauss-Sidel method, and tips for using Excel. The lastsection of the text covers the Visual Basic for Applications (VBA) techniques neededfor the book. The accompanying CD contains Excel worksheets and solutions toend-of-chapter exercises.Simon Benninga is Dean of the Facultyand Professor of Finance at Tel Aviv University and Visiting Professor of Finance atthe Wharton School at the University of Pennsylvania.

    Includes expert coverage of new topics: Turbocodes, Turboequalization, Antenna Arrays, Digital Cellular Systems, and Iterative Detection. Convenient, sequential organization begins with a look at the historyo and classification of channel models and builds from there.

    government began standardizing and regulating financial reporting in 1929 when the stock market crash made it painfully clear that businesses often made absurd claims and that investors were either gullible, unable to verify information, or both. Now, financial reports are used by a company's management to measure profitability (or lack of it), optimize operations and guide the company, by banks and other lenders to gauge the company's financial health, and by institutional or individual investors interested in purchasing stock. Unless you're financially savvy, annual reports with all those figures, frustrating footnotes, and fine print are boring and intimidating. However, once you have a fundamental knowledge of finance and its basic terminology, you can find the juicy parts. Reading Financial Reports For Dummies by Lita Epstein, a teacher of online financial courses and author of Trading for Dummies, gets you up to speed so you can:Go past the prose that can maximize the positive and minimize the negative and get information in dollars and cents Get an overview from the big three--the balance sheet, income statement, and statement of cash flows Understand the lingo and read between the lines Calculate basics like PE, Dividend Payout Ratio, ROS, ROA, ROE, Operating Margin, and Net Margin It pays for investors to be somewhat skeptical instead of gullible. Pressured to please Wall Street, companies are sometimes tempted to use "creative" accounting. You'll discover how to:Detect red flags (that, unfortunately, aren't emphasized in red) such as lawsuits, changes in accounting methods, and obligations to retirees and future retirees Understand the different reporting requirements for public companies and private companies with various types of business structures Analyze a company's cash flow, a prime indicator of its financial health Scrutinize deals such as mergers, acquisitions, liquidations and other major changes in key assets Organized so you can start where you're comfortable and proceed at your own pace, Reading Financial Reports for Dummies helps managers prepare annual reports and use financial reporting to budget more efficiently and helps investors base their decisions on knowledge instead of hype. Whether you're in business or in the stock market, knowledge is always an asset.

    In addition to step-by-step coverage of documentation, the book includes an overview of the research-writing process entitled "Writing Scholarly Papers" and three useful appendices. Thorough and practical, this convenient reference guide is also less expensive and easier for undergraduates to use than the APA Manual. The Second Edition features expanded coverage of electronic sources to keep students up-to-date on using and evaluating Internet references in their research. In addition, this new edition provides more guidance on avoiding plagiarism. The two sample APA-style papers--one argumentative and one experimental--are carefully annotated to give students extra support as they master the elements of manuscript preparation and documentation principles.

    Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

    "Corporate Finance For Dummies" introduces you to the practices of determining an operating budget, calculating future cash flow, and scenario analysis in a friendly, un-intimidating way that makes comprehension easy."Corporate Finance For Dummies" covers everything you'll encounter in a course on corporate finance, including accounting statements, cash flow, raising and managing capital, choosing investments; managing risk; determining dividends; mergers and acquisitions; and valuation.Serves as an excellent resource to supplement coursework related to corporate financeGives you the tools and advice you need to understand corporate finance principles and strategiesProvides information on the risks and rewards associated with corporate finance and lendingWith easy-to-understand explanations and examples, "Corporate Finance For Dummies" is a helpful study guide to accompany your coursework, explaining the tough stuff in a way you can understand.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Just about everyone has at least heard of Albert Einstein's formulation of 1905, which came into the world as something of an afterthought. But far fewer can explain his insightful linkage of energy to mass. David Bodanis offers an easily grasped gloss on the equation. Mass, he writes, "is simply the ultimate type of condensed or concentrated energy," whereas energy "is what billows out as an alternate form of mass under the right circumstances." Just what those circumstances are occupies much of Bodanis's book, which pays homage to Einstein and, just as important, to predecessors such as Maxwell, Faraday, and Lavoisier, who are not as well known as Einstein today. Balancing writerly energy and scholarly weight, Bodanis offers a primer in modern physics and cosmology, explaining that the universe today is an expression of mass that will, in some vastly distant future, one day slide back to the energy side of the equation, replacing the "dominion of matter" with "a great stillness"--a vision that is at once lovely and profoundly frightening. Without sliding into easy psychobiography, Bodanis explores other circumstances as well; namely, Einstein's background and character, which combined with a sterling intelligence to afford him an idiosyncratic view of the way things work--a view that would change the world. --Gregory McNamee

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    It stresses the economic intuition behind the subject matter while presenting advanced concepts of investment analysis and portfolio management. Readers will also discover the strengths and weaknesses of modern portfolio theory as well as the latest breakthroughs.

    Visually engaging, enticing and current examples with an overall focus on business.Legal Environment of Business and E-Commerce; Torts, Crimes, and Intellectual Property; Contracts and E-Commerce; Domestic and International Sales and Lease Contracts; Negotiable Instruments and E-Money; Credit, Secured Transactions, and Bankruptcy; Agency and Employment; Business Organizations and Ethics; Government Regulation; Property; Special Topics; Global EnvironmentMARKET Business Law continues its dedication to being the most engaging text for readers by featuring a visually appealing format with enticing and current examples while maintaining its focus on business.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f