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Bayesian Logical Data Analysis for the Physical Sciences, Gregory


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Автор: Gregory
Название:  Bayesian Logical Data Analysis for the Physical Sciences
Перевод названия: Грегори: Байесов логический анализ данных в физических науках
ISBN: 9780521150125
Издательство: Cambridge Academ
Классификация:

ISBN-10: 0521150124
Обложка/Формат: Paperback
Страницы: 488
Вес: 0.83 кг.
Дата издания: 20.05.2010
Серия: Statistics and applied probability
Язык: English
Иллюстрации: 132 b/w illus. 74 exercises
Размер: 245 x 175 x 25
Читательская аудитория: physical sciences, biological sciences, applied mathematics, statistics
Основная тема: Statistics and probability
Подзаголовок: A Comparative Approach with Mathematica® Support
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica (R) notebooks are available.

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
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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.

Data Reduction And Error Analysis For The Physical Sciences

Автор: Bevington; Robinson
Название: Data Reduction And Error Analysis For The Physical Sciences
ISBN: 0071199268 ISBN-13(EAN): 9780071199261
Издательство: McGraw-Hill
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Цена: 49180.00 T
Наличие на складе: Поставка под заказ.
Описание: Provides an introduction to the concepts of statistical analysis of data for students at undergraduate and graduate level. This text also provides tools for data reduction and error analysis commonly required in the physical sciences. It features a variety of numerical and graphical techniques, and emphasizes methods of handling data than theory.

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
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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 Nonparametric Data Analysis

Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 79190.00 T
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.


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