Dealing with measure theory and Lebesque integration, this is an introductory graduate text.

    And  a grouping of four stories at the end follows a divorced psychiatrist in an arc that constitutes a short novel.   Included are: “His Women,”  “Great Sex,”  “Old Love Affairs,” and  “The Drinking Club,”  “Patients, “The Wrong Mexico, “ and “Earthquake Damage.”

    The book will also be extremely useful for those preparing for other MBA entrance examinations like XAT, SNAP, CMAT, NMAT, etc. Quantitative Aptitude is quite challenging component of the CAT question paper and the other mentioned MBA entrance examinations. In his inimitable style, Arun Sharma, an acknowledged authority on the topic, provides a comprehensive package of theory and practice problems to enable aspirants to attempt questions with extra speed and confidence.

    

    The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association. The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

    The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.

    KEY TOPICS: The major changes of the revision are reorganization of chapter material and the addition of a much wider range of difficulties.

    The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The text gives both precise statements of results, plausibility arguments, and even some proofs, but more importantly intuitive explanations developed and refine through classroom experience with this material are provided. The book includes a self-contained treatment of the probability theory needed for shastic calculus, including Brownian motion and its properties. Advanced topics include foreign exchange models, forward measures, and jump-diffusion processes.This book is being published in two volumes. This second volume develops shastic calculus, martingales, risk-neutral pricing, exotic options and term structure models, all in continuous time.Masters level students and researchers in mathematical finance and financial engineering will find this book useful.Steven E. Shreve is Co-Founder of the Carnegie Mellon MS Program in Computational Finance and winner of the Carnegie Mellon Doherty Prize for sustained contributions to education.

    It's time we started treating it like one.Science has offered undisputed proof that alcoholism is a disease rather than a weakness of character, yet millions of alcoholics continue to suffer due to inappropriate treatment.  Now the co-author of the modern classic Under the Influence has teamed up with prominent alcoholism experts to provide new answers to this national epidemic.Based on the latest scientific research, Beyond the Influence clearly explains the neurological nature of the disease and reveals why some people drink addictively and others do not.  It also spells out what needs to be done to treat alcoholism, including: Steps to take for an intervention How to find the right treatment program Which psychological approaches work best Why spirituality is essential to recovery New insights into relapse prevention What you should know about diet, exercise, and nontraditional treatments such as acupunctureProvocative and eye-opening, compelling and compassionate, Beyond the Influence is not only a message of hope for alcoholics--it is a blueprint for saving lives.BEYOND THE INFLUENCE explains that alcoholism is a disease of the body, not a weakness of character. Drawing on the latest scientific studies, the authors present new research on the central role of genetics and neurotransmitters in addiction. Continuing where the prior book left off, it also includes: Steps for diagnosis and intervention, plus ways to prevent relapsesVarious treatment models, including inpatient and out-patient programs and a review of new drug treatmentsThe most effective types of psychological counselingThe critical role of nutritionNon-traditional healing methods for recoveryThe importance of a spiritual component to recoveryThe authors also critique our nation's alcoholism policies, including education and prevention programs, efforts to curtail college bingeing and underage drinking, and the advertising and marketing strategies of the alcohol industry. -->

    Other new features include a chapter on simulation, a section on Gibbs sampling, what you should know boxes at the end of each chapter, and remarks to highlight difficult concepts.

    Understanding Social Networks fills that gap by explaining the big ideas that underlie the social network phenomenon. Written for those interested in this fast moving area but who are not mathematically inclined, it covers fundamental concepts, then discusses networks and their core themes in increasing order of complexity. Kadushin demystifies the concepts, theories, and findings developed by network experts. He selects material that serves as basic building blocks and examples of best practices that will allow the reader to understand and evaluate new developments as they emerge. Understanding Social Networks will be useful to social scientists who encounter social network research in their reading, students new to the network field, as well as managers, marketers, and others who constantly encounter social networks in their work.

    KEY TOPICS: This classic book enables readers to make connections between classical and modern physics - an indispensable part of a physicist's education. In this new edition, Beams Medal winner Charles Poole and John Safko have updated the book to include the latest topics, applications, and notation, to reflect today's physics curriculum. They introduce readers to the increasingly important role that nonlinearities play in contemporary applications of classical mechanics. New numerical exercises help readers to develop skills in how to use computer techniques to solve problems in physics. Mathematical techniques are presented in detail so that the book remains fully accessible to readers who have not had an intermediate course in classical mechanics. MARKET: For college instructors and students.

    Part I introduces concepts of thermodynamics and statistical mechanics from a unified view. Parts II and III explore further applications of classical thermodynamics and statistical mechanics. Throughout, the emphasis is on real-world applications.

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.