Statistical Modeling and Computation, Dirk P. Kroese; Joshua C.C. Chan

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 10480.00 р.
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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: Kroese Dirk P
Название: Statistical Modeling and Computation
ISBN: 1461487749 ISBN-13(EAN): 9781461487746
Издательство: Springer
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Цена: 35173.00 р.
Наличие на складе: Нет в наличии.

Описание: This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models.

Автор: Malley
Название: Statistical Learning for Biomedical Data
ISBN: 0521699096 ISBN-13(EAN): 9780521699099
Издательство: Cambridge Academ
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Цена: 6494.00 р.
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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.

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 27712.00 р.
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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: Myers, Jerome L
Название: Research design and statistical analysis
ISBN: 0805864318 ISBN-13(EAN): 9780805864311
Издательство: Taylor&Francis
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Цена: 22202.00 р.
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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: Fung
Название: Statistical DNA Forensics - Theory, Methods and Computation
ISBN: 0470066369 ISBN-13(EAN): 9780470066362
Издательство: Wiley
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Цена: 14723.00 р.
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Описание: Statistical methodology plays a key role in ensuring that DNA evidence is collected, interpreted, analyzed, and presented correctly. With the recent advances in computer technology, this methodology is more complex than ever before. There are a growing number of books in the area but none are devoted to the computational analysis of evidence.

Автор: Sergei V. Chekanov
Название: Numeric Computation and Statistical Data Analysis on the Java Platform
ISBN: 3319285297 ISBN-13(EAN): 9783319285290
Издательство: Springer
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Цена: 12577.00 р.
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Описание: Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics are the key topics of this book. The short Python code examples powered by the Java platform can be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell.

Автор: Agarwal
Название: Statistical Methods for Recommender Systems
ISBN: 1107036070 ISBN-13(EAN): 9781107036079
Издательство: Cambridge Academ
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Цена: 7602.00 р.
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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.