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Mathematical Statistics, Rasch


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Цена: 10922.00р.
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При оформлении заказа до: 2026-05-14
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Автор: Rasch
Название:  Mathematical Statistics
ISBN: 9781119385288
Издательство: Wiley
Классификация:
ISBN-10: 1119385288
Обложка/Формат: Hardback
Страницы: 688
Вес: 0.98 кг.
Дата издания: 2018
Язык: English
Размер: 161 x 238 x 37
Читательская аудитория: Professional & vocational
Основная тема: Probability & Mathematical Statistics
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Англии


The Elements of Statistical Learning

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: 10061.00 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.

Computer Age Statistical Inference, Student Edition

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 5069.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Mathematics for Machine Learning

Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Название: Mathematics for Machine Learning
ISBN: 110845514X ISBN-13(EAN): 9781108455145
Издательство: Cambridge Academ
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Цена: 6334.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.

Data-driven science and engineering

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 7918.00 р.
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Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.

All the Math You Missed

Автор: Thomas A. Garrity
Название: All the Math You Missed
ISBN: 1009009192 ISBN-13(EAN): 9781009009195
Издательство: Cambridge Academ
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Цена: 3960.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The second edition of this bestselling book provides an overview of the key topics in undergraduate mathematics, allowing beginning graduate students to fill in any gaps in their knowledge. With numerous examples, exercises and suggestions for further reading, it is a must-have for anyone looking to learn some serious mathematics quickly.

Mathematical statistics with applications

Автор: Wackerly, Dennis Mendenhall, William Scheaffer, Ri
Название: Mathematical statistics with applications
ISBN: 0495385069 ISBN-13(EAN): 9780495385066
Издательство: Cengage Learning
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Цена: 11947.00 р.
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Описание: Prepare for exams and succeed in your mathematics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in MATHEMATICAL STATISTICS WITH APPLICATIONS, 7th Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples.

Basics of moderm mathematical Statistics

Автор: Spokoiny Vladimir, Dickhaus Thorsten
Название: Basics of moderm mathematical Statistics
ISBN: 366251348X ISBN-13(EAN): 9783662513484
Издательство: Springer
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Цена: 6829.00 р. 9756.00 -30%
Наличие на складе: Есть (1 шт.)
Описание: The present book provides a fully self-contained introduction to the world of modern mathematical statistics, collecting the basic knowledge, concepts and findings needed for doing further research in the modern theoretical and applied statistics.

Measuring and Reasoning

Автор: Bookstein, Fred L.,
Название: Measuring and Reasoning
ISBN: 1107024153 ISBN-13(EAN): 9781107024151
Издательство: Cambridge Academ
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Цена: 7760.00 р.
Наличие на складе: Поставка под заказ.

Описание: This exploration of empirical inference in science ranges over topics as diverse as the mass extinction of the dinosaurs, Peirce`s concept of abduction, multiple regression, and the analysis of patterns in astrophysics. At its heart is a formal description of the process by which scientific measurements support convincing explanations of the world around us.

Modeling and Reasoning with Bayesian Networks

Автор: Darwiche
Название: Modeling and Reasoning with Bayesian Networks
ISBN: 1107678420 ISBN-13(EAN): 9781107678422
Издательство: Cambridge Academ
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Цена: 9821.00 р.
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Описание: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.

Dynamic Documents with R and knitr, Second Edition

Автор: Xie Y.
Название: Dynamic Documents with R and knitr, Second Edition
ISBN: 1498716962 ISBN-13(EAN): 9781498716963
Издательство: Taylor&Francis
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Цена: 12401.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Quickly and Easily Write Dynamic Documents

Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package.

New to the Second Edition

  • A new chapter that introduces R Markdown v2
  • Changes that reflect improvements in the knitr package
  • New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents

Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible

Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.

Data Science with Julia

Автор: Mcnicholas
Название: Data Science with Julia
ISBN: 1138499986 ISBN-13(EAN): 9781138499980
Издательство: Taylor&Francis
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Цена: 9186.00 р.
Наличие на складе: Нет в наличии.

Описание: There is a dearth of resources for data scientists, statisticians, etc., wishing to learn about Julia. Using well known data science methods, this book will both motivate the reader and assuage any unease. The book will get readers up to speed on key features of the Julia language and illustrate some of its advantages for data science work.

Spectral Analysis for Univariate Time Series

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 14573.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.


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