Uncertainty: The Soul of Modeling, Probability & Statistics, Briggs William
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: 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: Lindley Название: Understanding Uncertainty, Revised Edition ISBN: 1118650123 ISBN-13(EAN): 9781118650127 Издательство: Wiley Рейтинг: Цена: 15674.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With a focus on understanding and using probability calculations, Understanding Uncertainty demystifies probability and explains in straightforward detail the logic of uncertainty, its truths, and its falsehoods.
Автор: Kadane Название: Pragmatics Of Uncertainty ISBN: 1498719848 ISBN-13(EAN): 9781498719841 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.
Автор: Compte Olivier, Postlewaite Andrew Название: Ignorance and Uncertainty ISBN: 1108434495 ISBN-13(EAN): 9781108434492 Издательство: Cambridge Academ Рейтинг: Цена: 5386.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Compte and Postlewaite propose novel methods to incorporate ignorance and uncertainty into economic modeling, without complex mathematics. An accessible text that proposes a constructive critique of the discipline, and that will find a broad audience with readers who build or use economic models, and those just interested in the discipline.
Автор: Compte Olivier, Postlewaite Andrew Название: Ignorance and Uncertainty ISBN: 1108422020 ISBN-13(EAN): 9781108422024 Издательство: Cambridge Academ Рейтинг: Цена: 15682.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Compte and Postlewaite propose novel methods to incorporate ignorance and uncertainty into economic modeling, without complex mathematics. An accessible text that proposes a constructive critique of the discipline, and that will find a broad audience with readers who build or use economic models, and those just interested in the discipline.
Автор: Morrison Faith A. Название: Uncertainty Analysis for Engineers and Scientists ISBN: 1108478352 ISBN-13(EAN): 9781108478359 Издательство: Cambridge University Press Рейтинг: Цена: 23059.00 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.
Автор: Martin, Ryan Название: Inferential Models ISBN: 0367737809 ISBN-13(EAN): 9780367737801 Издательство: Taylor&Francis Рейтинг: Цена: 7808.00 р. Наличие на складе: Нет в наличии.
Автор: Donald B. Percival, Andrew T. Walden Название: Spectral Analysis for Univariate Time Series ISBN: 1107028140 ISBN-13(EAN): 9781107028142 Издательство: Cambridge Academ Рейтинг: 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.
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: Mukherjee Название: Analytical Modeling of Heterogeneous Cellular Networks ISBN: 1107050944 ISBN-13(EAN): 9781107050945 Издательство: Cambridge Academ Рейтинг: Цена: 8710.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive, self-contained introduction to the use of stochastic geometry techniques for studying the behaviour of heterogeneous cellular networks presents a range of analytic results and approaches, including the mathematical tools and techniques used to derive these results. A valuable reference for industry practitioners, graduate students and researchers.
Автор: Joe Harry Название: Dependence Modeling with Copulas ISBN: 1466583223 ISBN-13(EAN): 9781466583221 Издательство: Taylor&Francis Рейтинг: Цена: 15310.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection.
The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.
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