Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    Their dedication and hard work have yielded an authoritative volume that defines and classifies mental disorders in order to improve diagnoses, treatment, and research.The criteria are concise and explicit, intended to facilitate an objective assessment of symptom presentations in a variety of clinical settings -- inpatient, outpatient, partial hospital, consultation-liaison, clinical, private practice, and primary care. New features and enhancements make DSM-5 easier to use across all settings:- The chapter organization reflects a lifespan approach, with disorders typically diagnosed in childhood (such as neurodevelopmental disorders) at the beginning of the manual, and those more typical of older adults (such as neurocognitive disorders) placed at the end. Also included are age-related factors specific to diagnosis. - The latest findings in neuroimaging and genetics have been integrated into each disorder along with gender and cultural considerations.- The revised organizational structure recognizes symptoms that span multiple diagnostic categories, providing new clinical insight in diagnosis. - Specific criteria have been streamlined, consolidated, or clarified to be consistent with clinical practice (including the consolidation of autism disorder, Asperger's syndrome, and pervasive developmental disorder into autism spectrum disorder; the streamlined classification of bipolar and depressive disorders; the restructuring of substance use disorders for consistency and clarity; and the enhanced specificity for major and mild neurocognitive disorders).- Dimensional assessments for research and validation of clinical results have been provided.- Both ICD-9-CM and ICD-10-CM codes are included for each disorder, and the organizational structure is consistent with the new ICD-11 in development.The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, is the most comprehensive, current, and critical resource for clinical practice available to today's mental health clinicians and researchers of all orientations. The information contained in the manual is also valuable to other physicians and health professionals, including psychologists, counselors, nurses, and occupational and rehabilitation therapists, as well as social workers and forensic and legal specialists.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    The authors take time to explain statistical procedures so that you can go beyond memorizing formulas and gain a conceptual understanding of statistics. The authors also take care to show you how having an understanding of statistical procedures will help you comprehend published findings and will lead you to become a savvy consumer of information. Known for its exceptional accuracy and examples, this text also has a complete supplements package to support your learning.

    With cases studies found throughout, you will easily learn to apply principles to real life.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    . . It’s Complicated will update your mind.”—Alissa Quart, New York Times Book Review  “A fascinating, well-researched and (mostly) reassuring look at how today's tech-savvy teenagers are using social media.”—People  “The briefest possible summary? The kids are all right, but society isn’t.”—Andrew Leonard, Salon   What is new about how teenagers communicate through services such as Facebook, Twitter, and Instagram? Do social media affect the quality of teens’ lives? In this eye-opening book, youth culture and technology expert danah boyd uncovers some of the major myths regarding teens' use of social media. She explores tropes about identity, privacy, safety, danger, and bullying. Ultimately, boyd argues that society fails young people when paternalism and protectionism hinder teenagers’ ability to become informed, thoughtful, and engaged citizens through their online interactions. Yet despite an environment of rampant fear-mongering, boyd finds that teens often find ways to engage and to develop a sense of identity. Boyd’s conclusions are essential reading not only for parents, teachers, and others who work with teens but also for anyone interested in the impact of emerging technologies on society, culture, and commerce in years to come. Offering insights gleaned from more than a decade of original fieldwork interviewing teenagers across the United States, boyd concludes reassuringly that the kids are all right. At the same time, she acknowledges that coming to terms with life in a networked era is not easy or obvious. In a technologically mediated world, life is bound to be complicated.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.

    This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide.- UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELDCovers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin.- EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONSGives readers an understanding of how language-related algorithms can be applied to important real-world problems.- EMPHASIS ON SCIENTIFIC EVALUATIONOffers a description of how systems are evaluated with each problem domain.- EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSINGCovers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

    Drawing on in-depth observations of black and white middle-class, working-class, and poor families, Unequal Childhoods explores this fact, offering a picture of childhood today. Here are the frenetic families managing their children's hectic schedules of "leisure" activities; and here are families with plenty of time but little economic security. Lareau shows how middle-class parents, whether black or white, engage in a process of "concerted cultivation" designed to draw out children's talents and skills, while working-class and poor families rely on "the accomplishment of natural growth," in which a child's development unfolds spontaneously—as long as basic comfort, food, and shelter are provided. Each of these approaches to childrearing brings its own benefits and its own drawbacks. In identifying and analyzing differences between the two, Lareau demonstrates the power, and limits, of social class in shaping the lives of America's children.The first edition of Unequal Childhoods was an instant classic, portraying in riveting detail the unexpected ways in which social class influences parenting in white and African-American families. A decade later, Annette Lareau has revisited the same families and interviewed the original subjects to examine the impact of social class in the transition to adulthood.

    We have Mindstorms to thank for that. In this book, pioneering computer scientist Seymour Papert uses the invention of LOGO, the first child-friendly programming language, to make the case for the value of teaching children with computers. Papert argues that children are more than capable of mastering computers, and that teaching computational processes like de-bugging in the classroom can change the way we learn everything else. He also shows that schools saturated with technology can actually improve socialization and interaction among students and between students and teachers.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    Yin's bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful design and use of the case study research method. New to this Edition Includes 11 in-depth applications that show how researchers have implemented case study methods successfully. Increases reference to relativist and constructivist approaches to case study research, as well as how case studies can be part of mixed methods projects. Places greater emphasis on using plausible rival explanations to bolster case study quality. Discusses synthesizing findings across case studies in a multiple-case study in more detail Adds an expanded list of 15 fields that have text or texts devoted to case study research. Sharpens discussion of distinguishing research from non-research case studies. The author brings to light at least three remaining gaps to be filled in the future: how rival explanations can become more routinely integrated into all case study research; the difference between case-based and variable-based approaches to designing and analyzing case studies; and the relationship between case study research and qualitative research.