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Bayesian Statistics and New Generations, Raffaele Argiento; Daniele Durante; Sara Wade


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Автор: Raffaele Argiento; Daniele Durante; Sara Wade
Название:  Bayesian Statistics and New Generations
ISBN: 9783030306106
Издательство: Springer
Классификация:



ISBN-10: 3030306100
Обложка/Формат: Hardcover
Страницы: 184
Вес: 0.53 кг.
Дата издания: 2019
Серия: Springer Proceedings in Mathematics & Statistics
Язык: English
Иллюстрации: XI, 184 p. 40 illus., 29 illus. in color.
Размер: Book
Основная тема: Statistics
Подзаголовок: BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.
Дополнительное описание: Part I – Theory and Methods: A. Diana, J. Griffin, and E. Matechou, A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population.- S. Haque and K. Mengersen, Bias Estimation and Correction Using Bootstrap Simulation of the Linking Proc



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: 5702.00 р.
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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.

Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain

Автор: van Boekel M.A.J.S., Stein A., van Bruggen A.H.C.
Название: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain
ISBN: 1402019173 ISBN-13(EAN): 9781402019173
Издательство: Springer
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Цена: 15672.00 р.
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Описание: Proceedings of the Frontis workshop on Bayesian Statistics and quality modelling in the agro-food production chain, held in Wageningen, The Netherlands, 1-14 May 2003

Introduction to Probability, Second Edition

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: 11176.00 р.
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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.

Matrix Differential Calculus with Applications in Statistics and Econometrics

Автор: Jan R. Magnus, Heinz Neudecker
Название: Matrix Differential Calculus with Applications in Statistics and Econometrics
ISBN: 1119541204 ISBN-13(EAN): 9781119541202
Издательство: Wiley
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Цена: 14090.00 р.
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Описание:

A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics

This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.

Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.

  • Fulfills the need for an updated and unified treatment of matrix differential calculus
  • Contains many new examples and exercises based on questions asked of the author over the years
  • Covers new developments in field and features new applications
  • Written by a leading expert and pioneer of the theory
  • Part of the Wiley Series in Probability and Statistics

Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.

Probabilistic Finite Element Model Updating Using Bayesian Statistics

Автор: Marwala Tshilidzi
Название: Probabilistic Finite Element Model Updating Using Bayesian Statistics
ISBN: 1119153034 ISBN-13(EAN): 9781119153030
Издательство: Wiley
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Цена: 14565.00 р.
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Описание: Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure.

The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: * Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.

* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Fundamentals of Nonparametric Bayesian Inference

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 12989.00 р.
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.

Bayesian Methods in Biostatistics

Автор: Lesaffre
Название: Bayesian Methods in Biostatistics
ISBN: 0470018232 ISBN-13(EAN): 9780470018231
Издательство: Wiley
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Цена: 8862.00 р.
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Описание: * This book provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementations, with an emphasis on healthcare techniques. * Contains introductory explanations of Bayesian principles common to all areas.

Bayesian Statistics 2e

Название: Bayesian Statistics 2e
ISBN: 0470141158 ISBN-13(EAN): 9780470141151
Издательство: Wiley
Цена: 13226.00 р.
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Описание: Praise for the First Edition - 'I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics' - "Statistics in Medical Research". '[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics' - "STATS: The Magazine for Students of Statistics", American Statistical Association.

'Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike' - "Journal of Applied Statistics".The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. "Introduction to Bayesian Statistics, Second Edition" focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters.This book uniquely covers the topics typically found in an introductory statistics book-but from a Bayesian perspective-giving readers an advantage as they enter fields where statistics is used.

This Second Edition provides: an extended coverage of Poisson and Gamma distributions; two new chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations; a twenty-five percent increase in e ercises with selected answers at the end of the book; a calculus refresher appendix and a summary on the use of statistical tables; and, new computer exercises that use R functions and Minitab[registered] macros for Bayesian analysis and Monte Carlo simulations. "Introduction to Bayesian Statistics, Second Edition" is an invaluable textbook for advanced undergraduate and graduate-level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.

Computer Age Statistical Inference

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: 9029.00 р.
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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.

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: 11088.00 р.
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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.

Introduction to Mathematical Portfolio Theory

Автор: Joshi
Название: Introduction to Mathematical Portfolio Theory
ISBN: 1107042313 ISBN-13(EAN): 9781107042315
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.


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