Statistics in Research and Development, Caulcutt, R.
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10061.00 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: McElreath, Richard Название: Statistical Rethinking ISBN: 036713991X ISBN-13(EAN): 9780367139919 Издательство: Taylor&Francis Рейтинг: Цена: 13320.00 р. Наличие на складе: Заказано в издательстве.
Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: 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.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 9970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Field, Andy Название: Discovering statistics using ibm spss statistics ISBN: 1526419521 ISBN-13(EAN): 9781526419521 Издательство: Sage Publications Рейтинг: Цена: 6019.00 р. Наличие на складе: Поставка под заказ.
Описание: Unrivalled in the way it makes the teaching of statistics through the use of IBM SPSS statistics compelling and accessible to even the most anxious of students. The only statistics textbook you and your students will ever need just got better!
Автор: Lind Dougla Название: Basic Statistics for Business and Economics (2013 ed) ISBN: 0071318070 ISBN-13(EAN): 9780071318075 Издательство: McGraw-Hill Рейтинг: Цена: 9779.00 р. Наличие на складе: Поставка под заказ.
Описание: Presents a step-by-step approach that enhances student performance, accelerates preparedness and improves motivation for the student taking a business statistics course. This title provides students majoring in all fields of business administration with an introductory survey of the many applications of descriptive and inferential statistics.
Автор: Field, Andy Miles, Jeremy Field, Zoe Название: Discovering statistics using r ISBN: 1446289133 ISBN-13(EAN): 9781446289136 Издательство: Sage Publications Рейтинг: Цена: 9504.00 р. Наличие на складе: Поставка под заказ.
Описание: The R version of Andy Field`s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R - a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences.
Автор: Wasserman, Larry Название: All of statistics: A Concise Course in Statistical Inference ISBN: 1441923225 ISBN-13(EAN): 9781441923226 Издательство: Springer Рейтинг: Цена: 6951.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Автор: Morrison, Faith A. Название: Uncertainty analysis for engineers and scientists ISBN: 1108745741 ISBN-13(EAN): 9781108745741 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Whether you are new to the sciences or an experienced engineer, this useful text provides a practical approach to performing error analysis.
Описание: Statistics have helped shape every area of science. Without the means to analyze critical data, none of the great disoveries of the past would be possible. This paperback reprint of a Wiley bestseller shows the development of these data analysis tools and the manner in which they aided technological development prior to 1750.
Автор: Hand, David J. Название: Statistics ISBN: 019923356X ISBN-13(EAN): 9780199233564 Издательство: Oxford Academ Рейтинг: Цена: 1767.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistics has evolved into an exciting discipline which uses deep theory and powerful software to shed light on the world around us: from clinical trials in medicine, to economics, sociology, and countless other subjects vital to understanding modern life. This Very Short Introduction explores and explains how statistics works today.
Автор: Darwiche Название: Modeling and Reasoning with Bayesian Networks ISBN: 1107678420 ISBN-13(EAN): 9781107678422 Издательство: Cambridge Academ Рейтинг: Цена: 9821.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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