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.

    This book covers game theory, economics of Information, and behavioral economics, using examples that cover such topics as the analysis of demand, cost, and market efficiency; the design of pricing strategies; and public policy analysis.

    Applauded for providing the most clear and accurate presentation of advanced microeconomic concepts, it offers an ideal level of mathematical rigor for upper level undergraduate students and beginning graduate students. It gives students the opportunity to work directly with theoretical tools, real-world applications, and cutting edge developments in the study of microeconomics. This text is solid, rigorous, comprehensive, and is sensibly challenging for students, best serving students with a mathematics background.

    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.

    And who could blame them? These so-called millennials have come of age in the wake of the worst economic crisis in memory, and are now trying to get by in its aftermath. They owe record levels of student loan debt, face sky-high rents, and struggle to live on a budget in an uncertain economy. It’s time for them to get a financial life. For two decades, Beth Kobliner’s bestseller has been the financial bible for people in their twenties and thirties. With her down-to-earth style, she has taught them how to get out of debt, learn to save, and invest for their futures. In this completely revised and updated edition, Kobliner shares brand-new insights and concrete, actionable advice geared to help a new generation of readers form healthy financial habits that will last a lifetime. With fresh material that reflects the changing digital world, Get a Financial Life remains an essential tool for young people learning how to manage their money. From tackling taxes to boosting credit scores, Get a Financial Life can show those just starting out how to decrease their debt, avoid common money mistakes, and navigate the world of personal finance in today’s ever-changing landscape.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    In 1923, with its currency effectively worthless (the exchange rate in December of that year was one dollar to 4,200,000,000,000 marks), the German republic was all but reduced to a barter economy. Expensive cigars, artworks, and jewels were routinely exchanged for staples such as bread; a cinema ticket could be bought for a lump of coal; and a bottle of paraffin for a silk shirt. People watched helplessly as their life savings disappeared and their loved ones starved. Germany’s finances descended into chaos, with severe social unrest in its wake. Money may no longer be physically printed and distributed in the voluminous quantities of 1923. However, “quantitative easing,” that modern euphemism for surreptitious deficit financing in an electronic era, can no less become an assault on monetary discipline. Whatever the reason for a country’s deficit necessity or profligacy, unwillingness to tax or blindness to expenditure it is beguiling to suppose that if the day of reckoning is postponed economic recovery will come in time to prevent higher unemployment or deeper recession. What if it does not? Germany in 1923 provides a vivid, compelling, sobering moral tale.

    Therefore, this book provides the fundamental concepts and techniques of real analysis for readers in all of these areas. It helps one develop the ability to think deductively, analyze mathematical situations and extend ideas to a new context. Like the first two editions, this edition maintains the same spirit and user-friendly approach with some streamlined arguments, a few new examples, rearranged topics, and a new chapter on the Generalized Riemann Integral.

    The reliance traders put on such quantitative analysis was catastrophic for the economy, setting off the series of financial crises that began to erupt in 2007 with the mortgage crisis and from which we're still recovering. Here Derman looks at why people--bankers in particular--still put so much faith in these models, and why it's a terrible mistake to do so.Though financial models imitate the style of physics by using the language of mathematics, ultimately they deal with human beings. Their similarity confuses the fundamental difference between the aims and possible achievements of the phsyics world and that of the financial world. When we make a model involving human beings, we are trying to force the ugly stepsister's foot into Cinderella's pretty glass slipper.  It doesn't fit without cutting off some of the essential parts. Physicists and economists have been too enthusiastic to recognize the limits of their equations in the sphere of human behavior--which of course is what economics is all about.  Models.Behaving.Badly. includes a personal account Derman's childhood encounter with failed models--the utopia of the kibbutz, his experience as a physicist on Wall Street, and a look at the models quants generated: the benefits they brought and the problems they caused. Derman takes a close look at what a model is, and then he highlights the differences between the success of modeling in physics and its relative failure in economics.  Describing the collapse of the subprime mortgage CDO market in 2007, Derman urges us to stop relying on these models where possible, and offers suggestions for mending these models where they might still do some good.  This is a fascinating, lyrical, and very human look behind the curtain at the intersection between mathematics and human nature.

    It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

    Sachs-one of the world's most respected economists and the author of The New York Times bestseller The End of Poverty- offers an urgent assessment of the environmental degradation, rapid population growth, and extreme poverty that threaten global peace and prosperity. Through crystalline examination of hard facts, Sachs predicts the cascade of crises that awaits this crowded planet-and presents a program of sustainable development and international cooperation that will correct this dangerous course. Few luminaries anywhere on the planet are as schooled in this daunting subject as Sachs, and this is the vital product of his experience and wisdom.