Statistical Methods for Spoken Dialogue Management, Blaise Thomson
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Malley Название: Statistical Learning for Biomedical Data ISBN: 0521699096 ISBN-13(EAN): 9780521699099 Издательство: Cambridge Academ Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.
Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Автор: Agarwal Название: Statistical Methods for Recommender Systems ISBN: 1107036070 ISBN-13(EAN): 9781107036079 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Описание: This book describes a system of mathematical models and methods that can be used to analyze real economic and managerial decisions and to improve their effectiveness.
Автор: Lai, Tze Leung Xing, Haipeng Название: Statistical models and methods for financial markets ISBN: 1441926682 ISBN-13(EAN): 9781441926685 Издательство: Springer Рейтинг: Цена: 10335.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The authors here present statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. They provide basic statistical background as well as in-depth applications.
Автор: Myers, Jerome L Название: Research design and statistical analysis ISBN: 0805864318 ISBN-13(EAN): 9780805864311 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Поставка под заказ.
Описание: This interdisciplinary group of scholars-anthropologists, archaeologists, architects, educators, lawyers, heritage administrators, policy analysts, and consultants-make the first attempt to define and assess heritage values on a local, national and global level. Chapters range from the theoretical to policy frameworks to case studies of heritage practice, written by scholars from eight countries.
Автор: J. Philip Miller Название: Essential Statistical Methods for Medical Statistics, ISBN: 0444537376 ISBN-13(EAN): 9780444537379 Издательство: Elsevier Science Рейтинг: Цена: 8541.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Addresses statistical challenges in epidemiological, biomedical, and pharmaceutical research. This book presents methods for assessing Biomarkers, analysis of competing risks. It offers clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs.
Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors' experience.
Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat(R) statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R.
By the time you reach the end of the book (and online material) you will have gained:
A clear appreciation of the importance of a statistical approach to the design of your experiments,
A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables,
Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly,
An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working.
The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.
Описание: Vol.4: Effective Environmental Management for Sustainable Development
Автор: Hastie Название: Statistical Learning with Sparsity ISBN: 1498712169 ISBN-13(EAN): 9781498712163 Издательство: Taylor&Francis Рейтинг: Цена: 16843.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Discover New Methods for Dealing with High-Dimensional Data
A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.
Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.
In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
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