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Python for Probability, Statistics, and Machine Learning, Unpingco Josй


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Цена: 51230.00T
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Склад Америка: 226 шт.  
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
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Автор: Unpingco Josй
Название:  Python for Probability, Statistics, and Machine Learning
ISBN: 9783030185473
Издательство: Springer
Классификация:





ISBN-10: 3030185478
Обложка/Формат: Paperback
Страницы: 384
Вес: 0.56 кг.
Дата издания: 14.08.2020
Язык: English
Издание: 2nd ed. 2019
Иллюстрации: 38 tables, color; 37 illustrations, color; 128 illustrations, black and white; xiv, 384 p. 165 illus., 37 illus. in color.
Размер: 23.39 x 15.60 x 2.08 cm
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.

Scientific Method in Brief

Автор: Gauch, Jr
Название: Scientific Method in Brief
ISBN: 1107666724 ISBN-13(EAN): 9781107666726
Издательство: Cambridge Academ
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Цена: 43290.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Specifically designed to enhance perspective and encourage productivity, this is a guide to the key principles of scientific method including deductive and inductive logic, probability, parsimony and hypothesis testing. The examples and case studies span the physical, biological and social sciences and also highlight science`s interrelationship with the humanities.

Counterfactuals and Causal Inference

Автор: Morgan
Название: Counterfactuals and Causal Inference
ISBN: 1107694167 ISBN-13(EAN): 9781107694163
Издательство: Cambridge Academ
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Цена: 38010.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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.

Probability and Statistics for Engineering and the Sciences

Автор: Devore Jay L.
Название: Probability and Statistics for Engineering and the Sciences
ISBN: 1337094269 ISBN-13(EAN): 9781337094269
Издательство: Cengage Learning
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Цена: 71800.00 T
Наличие на складе: Нет в наличии.
Описание: Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION. Always a market favorite, this calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces. Jay Devore, an award-winning professor and internationally recognized author and statistician, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations.

Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate book, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION also includes graphics and screen shots from SAS (R), MINITAB (R), and Java (TM) Applets to give you a solid perspective of statistics in action.


Bayesian Data Analysis, Third Edition

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
Наличие на складе: Есть у поставщика Поставка под заказ.
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.

The Analysis of Biological Data

Автор: Michael C. Whitlock, Dolph Schluter
Название: The Analysis of Biological Data
ISBN: 1319325343 ISBN-13(EAN): 9781319325343
Издательство: Macmillan Learning
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Цена: 92390.00 T
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Описание: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.

Bayesian Analysis with Stata

Автор: Thompson John
Название: Bayesian Analysis with Stata
ISBN: 1597181412 ISBN-13(EAN): 9781597181419
Издательство: Taylor&Francis
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Цена: 57150.00 T
Наличие на складе: Невозможна поставка.
Описание:

Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.

The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.

The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.


Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 73920.00 T
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Python for Probability, Statistics, and Machine Learning

Автор: Unpingco Jose
Название: Python for Probability, Statistics, and Machine Learning
ISBN: 3030185443 ISBN-13(EAN): 9783030185442
Издательство: Springer
Рейтинг:
Цена: 55890.00 T
Наличие на складе: Невозможна поставка.
Описание: ¢ Are you member of either the management team or the board and do you see the need to adapt your organization to Agile? Do you intend to make the organization more agile? ¢ Are you a coach helping organizations in the transformation to becoming more agile? And are you planning to support this transition using a bottom-up or top-down approach?In this pocket guide you will find a practical approach on how to handle this. Governing an organization in a fast-changing world. And all this although the issues of the day require a lot of your attention and can distract you from the results you want to achieve. The authors consider how to operationalize the organization`s strategic goals and consequently the governance of the entire organization.The authors start from the position of: ¢ Clarifying what has to be achieved in the next quarter in order to achieve the strategic goals. ¢ Introducing a system of short cyclical adjustments, with which you can respond to changing demand from customers or emerging laws and regulations. ¢ Working closely together as management team or board towards the long-term strategic goals and preventing everyone within the organization from following their own goals. ¢ Bringing more focus on the operationalization of the strategy, less `fire-fighting` and greater emphasis on fire prevention. ¢ Getting a clear picture of what prevents your employees from doing their jobs effectively. Will you succeed in removing the barriers holding back your organization? The core message of this pocket guide is application of the FOCUS board. This is a visual approach to management and a strong tool for governing the organization. When this is applied, it will result in collaboration between all layers of the organization, enable short cyclical adjustment and provide a clear focus on achieving the strategic goals.

Weighing the odds

Автор: David Williams
Название: Weighing the odds
ISBN: 052100618X ISBN-13(EAN): 9780521006187
Издательство: Cambridge Academ
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Цена: 79200.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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.

Meta-Analysis in Stata

Автор: Tom M. Palmer and Jonathan A. C. Sterne (editors)
Название: Meta-Analysis in Stata
ISBN: 1597181471 ISBN-13(EAN): 9781597181471
Издательство: Taylor&Francis
Рейтинг:
Цена: 77570.00 T
Наличие на складе: Невозможна поставка.
Описание: Meta-analysis allows researchers to combine the results of several studies into a unified analysis that provides an overall estimate of the effect of interest. This collection of articles from the Stata Journal and Stata Technical Bulletin will be indispensable to researchers who wish to conduct meta-analyses using Stata and learn about the full range of user-written Stata meta-analysis commands. With these articles and the associated Stata software, you gain access to the statistical methods behind the rapid increase in the number of meta-analyses reported in the social and medical literature. Collectively, the articles provide a detailed description of a range of meta-analytic methods. They show how to conduct and interpret meta-analyses; how to produce highly flexible graphical displays; how to use meta-regression; how to examine bias; how to conduct individual participant data meta-analysis; and how to conduct multivariate meta-analysis. This edition also contains three articles on network metaanalysis, a major recent development in meta-analysis methodology.

Manifold Learning Theory and Applications

Автор: Ma
Название: Manifold Learning Theory and Applications
ISBN: 1439871094 ISBN-13(EAN): 9781439871096
Издательство: Taylor&Francis
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Цена: 148010.00 T
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Описание:

Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision.

Filling a void in the literature, Manifold Learning Theory and Applications incorporates state-of-the-art techniques in manifold learning with a solid theoretical and practical treatment of the subject. Comprehensive in its coverage, this pioneering work explores this novel modality from algorithm creation to successful implementation--offering examples of applications in medical, biometrics, multimedia, and computer vision. Emphasizing implementation, it highlights the various permutations of manifold learning in industry including manifold optimization, large scale manifold learning, semidefinite programming for embedding, manifold models for signal acquisition, compression and processing, and multi scale manifold.

Beginning with an introduction to manifold learning theories and applications, the book includes discussions on the relevance to nonlinear dimensionality reduction, clustering, graph-based subspace learning, spectral learning and embedding, extensions, and multi-manifold modeling. It synergizes cross-domain knowledge for interdisciplinary instructions, offers a rich set of specialized topics contributed by expert professionals and researchers from a variety of fields. Finally, the book discusses specific algorithms and methodologies using case studies to apply manifold learning for real-world problems.


One Hundred Nineteen Stata Tips

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
Наличие на складе: Нет в наличии.
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.


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