Probability Theory and Mathematical Statistics. Vol. 1, B. Grigelionis, V. A. Statulevicius, V. V. Sazonov, Yu. V. Prohorov

Автор: Morgan
Название: Counterfactuals and Causal Inference
ISBN: 1107694167 ISBN-13(EAN): 9781107694163
Издательство: Cambridge Academ
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Цена: 38010.00 T
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Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

Автор: Gilles Pisier
Название: Tensor Products of C*-Algebras and Operator Spaces: The Connes–Kirchberg Problem
ISBN: 1108479014 ISBN-13(EAN): 9781108479011
Издательство: Cambridge Academ
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Цена: 103490.00 T
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Описание: This advanced volume of lecture notes presents an open problem at the frontier of research into operator algebra theory, based on the author`s university lecture courses and written in a widely accessible style for researchers and Ph.D. students with little experience in the area.

Автор: Steven L. Brunton, J. Nathan Kutz
Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
ISBN: 1108422098 ISBN-13(EAN): 9781108422093
Издательство: Amazon Internet
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Цена: 0.00 T
Наличие на складе: Невозможна поставка.
Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.

Автор: Kerry B. Barker, Sandeep M. Menon, Ralph B. D`Agostino, Siyan Xu, Bo Jin
Название: Biosimilar Clinical Development: Scientific Considerations and New Methodologies
ISBN: 1482231697 ISBN-13(EAN): 9781482231694
Издательство: Taylor&Francis
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Цена: 132710.00 T
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Описание: Biosimilars have the potential to change the way we think about, identify, and manage health problems. They are already impacting both clinical research and patient care, and this impact will only grow as our understanding and technologies improve. Written by a team of experienced specialists in clinical development, this book discusses various potential drug development strategies, the design and analysis of pharmacokinetics (PK) studies, and the design and analysis of efficacy studies.

Автор: David Williams
Название: Weighing the odds
ISBN: 052100618X ISBN-13(EAN): 9780521006187
Издательство: Cambridge Academ
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Цена: 79200.00 T
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Описание: A lively book enriched with examples drawn from all manner of applications. Statistics chapters present both the Frequentist and Bayesian approaches, emphasising Confidence Intervals rather than Hypothesis Tests. C or WinBUGS code is provided for computational examples and simulations. Many exercises are included; hints or solutions are often provided.

Автор: Darwiche
Название: Modeling and Reasoning with Bayesian Networks
ISBN: 1107678420 ISBN-13(EAN): 9781107678422
Издательство: Cambridge Academ
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Цена: 65470.00 T
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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.

Автор: Deng Weihua
Название: Modeling Anomalous Diffusion: From Statistics To Mathematics
ISBN: 9811212996 ISBN-13(EAN): 9789811212994
Издательство: World Scientific Publishing
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Цена: 95040.00 T
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Описание:

This book focuses on modeling the anomalous diffusion phenomena, being ubiquitous in the natural world. Both the microscopic models (stochastic processes) and macroscopic models (partial differential equations) have been built up. The relationships between the two kinds of models are clarified, and based on these models, some statistical observables are analyzed. From statistics to mathematics, the built models show their power with their associated applications.

This book is important for students to develop basic skills to be able to succeed in their future research. In addition to introducing the related models or methods, it also provides the corresponding applications and simulation results, which will attract more readers ranging from mathematicians to physicists or chemists, to name a few.

Автор: Gross, Benedict H.,
Название: Fat chance :
ISBN: 1108728189 ISBN-13(EAN): 9781108728188
Издательство: Cambridge Academ
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Цена: 25350.00 T
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Описание: Designed for the intellectually curious, this book provides a solid foundation in basic probability theory in a charming style, without technical jargon. This text will immerse the reader in a mathematical view of the world, and teach them techniques to solve real-world problems both inside and outside the casino.

Автор: Gelman
Название: Bayesian Data Analysis, Third Edition
ISBN: 1439840954 ISBN-13(EAN): 9781439840955
Издательство: Taylor&Francis
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Цена: 73920.00 T
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Описание: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Автор: Cox
Название: One Hundred Nineteen Stata Tips
ISBN: 1597181439 ISBN-13(EAN): 9781597181433
Издательство: Taylor&Francis
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Цена: 50010.00 T
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Описание: One Hundred Nineteen Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003.

Автор: Joseph K. Blitzstein, Jessica Hwang
Название: Introduction to Probability, Second Edition
ISBN: 1138369918 ISBN-13(EAN): 9781138369917
Издательство: Taylor&Francis
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Цена: 74510.00 T
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Описание: Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.