High-dimensional data analysis with low-dimensional models, Wright, John (columbia University, New York) Ma, Yi (university Of California, Berkeley)
Автор: Currell Graham Название: Scientific Data Analysis ISBN: 0198712545 ISBN-13(EAN): 9780198712541 Издательство: Oxford Academ Рейтинг: Цена: 50680.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Drawing on the author`s extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.
Автор: Edmond Jennifer, Mandal Anthony, Horsley Nicola Название: The Trouble with Big Data: How Datafication Displaces Cultural Practices ISBN: 1350239623 ISBN-13(EAN): 9781350239623 Издательство: Bloomsbury Academic Рейтинг: Цена: 183920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.
The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.
Автор: Agresti, Alan, Название: An Introduction to Categorical Data Analysis, 3rd Edition ISBN: 1119405262 ISBN-13(EAN): 9781119405269 Издательство: Wiley Рейтинг: Цена: 128780.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A valuable new edition of a standard reference
The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.
Adding to the value in the new edition is:
- Illustrations of the use of R software to perform all the analyses in the book
- A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis
- New sections in many chapters introducing the Bayesian approach for the methods of that chapter
- More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets
- An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises
Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Автор: Pennington Diane Название: Social Tagging for Linking Data Across Environments ISBN: 1783303387 ISBN-13(EAN): 9781783303380 Издательство: Facet Рейтинг: Цена: 109120.00 T Наличие на складе: Нет в наличии. Описание: This book, representing researchers and practitioners across different information professions, will explore how social tags can link content across a variety of environments.
Автор: Janice L. Bishop, James F. Bell III, Jeffrey E. Mo Название: Remote compositional analysis : techniques for understanding spectroscopy, mineralogy, and geochemistry of planetary surfaces ISBN: 110718620X ISBN-13(EAN): 9781107186200 Издательство: Cambridge Academ Рейтинг: Цена: 108770.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a comprehensive overview of the theory and practical applications of spectroscopic, mineralogical, and geochemical techniques used in planetary remote sensing. It describes state-of-the-art developments in analyzing the chemistry and mineralogy of the surfaces of planets, moons, asteroids, and comets.
Автор: Amy Affelt Название: All That`s Not Fit to Print: Fake News and the Call to Action for Librarians and Information Professionals ISBN: 1789733642 ISBN-13(EAN): 9781789733648 Издательство: Emerald Рейтинг: Цена: 54550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Fake news may have reached new notoriety since the 2016 US election, but it has been around a long time. In All That`s Not Fit to Print, Amy Affelt offers tools and techniques for spotting fake news and discusses best practices for finding high quality sources, information, and data.
Автор: Pons, Odile, Название: Orthonormal series estimators / ISBN: 9811210683 ISBN-13(EAN): 9789811210686 Издательство: World Scientific Publishing Рейтинг: Цена: 95040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The approximation and the estimation of nonparametric functions by projections on an orthonormal basis of functions are useful in data analysis. This book presents series estimators defined by projections on bases of functions, they extend the estimators of densities to mixture models, deconvolution and inverse problems, to semi-parametric and nonparametric models for regressions, hazard functions and diffusions. They are estimated in the Hilbert spaces with respect to the distribution function of the regressors and their optimal rates of convergence are proved. Their mean square errors depend on the size of the basis which is consistently estimated by cross-validation. Wavelets estimators are defined and studied in the same models.The choice of the basis, with suitable parametrizations, and their estimation improve the existing methods and leads to applications to a wide class of models. The rates of convergence of the series estimators are the best among all nonparametric estimators with a great improvement in multidimensional models. Original methods are developed for the estimation in deconvolution and inverse problems. The asymptotic properties of test statistics based on the estimators are also established.
Автор: Subasi, Abdulhamit Название: Practical Machine Learning For Data Analysis Using Python ISBN: 0128213795 ISBN-13(EAN): 9780128213797 Издательство: Elsevier Science Рейтинг: Цена: 110030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.
Автор: Ali Tajer, Samir M. Perlaza, H. Vincent Poor Название: Advanced Data Analytics for Power Systems ISBN: 1108494757 ISBN-13(EAN): 9781108494755 Издательство: Cambridge Academ Рейтинг: Цена: 109830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Experts in data analytics and power engineering present theories addressing the needs of modern power systems. Covering theory and application to the solution of power system problems relating to reliability, efficiency, and security, this is an essential resource for graduate students and researchers in academia and industry.
Автор: Marc Potters, Jean-Philippe Bouchaud Название: A First Course in Random Matrix Theory ISBN: 1108488080 ISBN-13(EAN): 9781108488082 Издательство: Cambridge Academ Рейтинг: Цена: 61240.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Classical statistical tools that handled real-life data have become inadequate upon the emergence of Big Data. Random matrix theory and free calculus introduced here present valuable solutions to the complex challenges posed by large datasets. Real world applications make it an essential tool for physicists, engineers, data analysts and economists.
Автор: Johnny Golding, Martin Reinhart, Mattia Paganelli Название: Data Loam: Sometimes Hard, Usually Soft. The Future of Knowledge Systems ISBN: 3110680076 ISBN-13(EAN): 9783110680072 Издательство: Walter de Gruyter Рейтинг: Цена: 49530.00 T Наличие на складе: Невозможна поставка. Описание:
Als Reaktion auf die dominante Wirkkraft und Deutungshoheit des Digitalen vereint Data Loam auf der Basis von Positionen der internationalen zeitgenossischen Kunstpraxis radikale Denkansatze.
Vorbei: das Beharren auf Indexikalitat und die instrumentelle Reduktion des Wissens. Stattdessen: eine neue Metrik, die Spiel, Neugier, Experiment und Risiko fordert. Als dringende Antwort auf die stetig wachsende Informationsflut, der Bibliotheken, Suchmaschinen und kulturelle Einrichtungen ausgesetzt sind, werden Ansatze entwickelt, die sinnliche Logik, kausale Durchlassigkeit und neue Formen der Mensch-Maschine-Interaktion anregen und erlauben.
Data Loam beleuchtet die Zukunft von Wissenssystemen in Texten zu kunstlicher Intelligenz, Kybernetik und Kryptookonomie: als Gegenmittel zur Zerstreuung apokalyptischer Angste.
Автор: Wang Dashun, Barabбsi Albert-Lбszlу Название: The Science of Science ISBN: 1108492665 ISBN-13(EAN): 9781108492669 Издательство: Cambridge Academ Рейтинг: Цена: 131580.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is the first comprehensive overview of the exciting field of the `science of science`. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
