Objective Bayesian Inference
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Objective Bayesian Inference
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Author : James O Berger
language : en
Publisher: World Scientific
Release Date : 2024-03-06
Objective Bayesian Inference written by James O Berger and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-06 with Mathematics categories.
Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications.
Subjective And Objective Bayesian Statistics
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Author : S. James Press
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-25
Subjective And Objective Bayesian Statistics written by S. James Press and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-25 with Mathematics categories.
Ein Wiley-Klassiker über Bayes-Statistik, jetzt in durchgesehener und erweiterter Neuauflage! - Werk spiegelt die stürmische Entwicklung dieses Gebietes innerhalb der letzten Jahre wider - vollständige Darstellung der theoretischen Grundlagen - jetzt ergänzt durch unzählige Anwendungsbeispiele - die wichtigsten modernen Methoden (u. a. hierarchische Modellierung, linear-dynamische Modellierung, Metaanalyse, MCMC-Simulationen) - einzigartige Diskussion der Finetti-Transformierten und anderer Themen, über die man ansonsten nur spärliche Informationen findet - Lösungen zu den Übungsaufgaben sind enthalten
Objective Bayesian Inference In General Generalized Linear Mixed Models Using Reference Priors
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Author : Xin Zhao
language : en
Publisher:
Release Date : 2005
Objective Bayesian Inference In General Generalized Linear Mixed Models Using Reference Priors written by Xin Zhao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Bayesian Inference
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Author : William A Link
language : en
Publisher: Academic Press
Release Date : 2009-08-07
Bayesian Inference written by William A Link and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-07 with Science categories.
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analytical software and examples Leading authors with world-class reputations in ecology and biostatistics
An Introduction To Bayesian Analysis
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Author : Jayanta K. Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-03
An Introduction To Bayesian Analysis written by Jayanta K. Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-03 with Mathematics categories.
Though there are many recent additions to graduate-level introductory books on Bayesian analysis, none has quite our blend of theory, methods, and ap plications. We believe a beginning graduate student taking a Bayesian course or just trying to find out what it means to be a Bayesian ought to have some familiarity with all three aspects. More specialization can come later. Each of us has taught a course like this at Indian Statistical Institute or Purdue. In fact, at least partly, the book grew out of those courses. We would also like to refer to the review (Ghosh and Samanta (2002b)) that first made us think of writing a book. The book contains somewhat more material than can be covered in a single semester. We have done this intentionally, so that an instructor has some choice as to what to cover as well as which of the three aspects to emphasize. Such a choice is essential for the instructor. The topics include several results or methods that have not appeared in a graduate text before. In fact, the book can be used also as a second course in Bayesian analysis if the instructor supplies more details. Chapter 1 provides a quick review of classical statistical inference. Some knowledge of this is assumed when we compare different paradigms. Following this, an introduction to Bayesian inference is given in Chapter 2 emphasizing the need for the Bayesian approach to statistics.
Handbook Of Bayesian Fiducial And Frequentist Inference
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Author : James Berger
language : en
Publisher: CRC Press
Release Date : 2024-02-26
Handbook Of Bayesian Fiducial And Frequentist Inference written by James Berger and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Mathematics categories.
The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference. Key Features: Provides a comprehensive introduction to the key developments in the BFF schools of inference Gives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledge Is accessible for readers with different perspectives and backgrounds
Bayesian Inference
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-01
Bayesian Inference written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Computers categories.
What Is Bayesian Inference Bayesian inference is a type of statistical inference that updates the probability of a hypothesis based on new data or information using Bayes' theorem. This way of statistical inference is known as the Bayesian method. In the field of statistics, and particularly in the field of mathematical statistics, the Bayesian inference method is an essential tool. When conducting a dynamic analysis of a data sequence, bayesian updating is an especially useful technique to utilize. Inference based on Bayes' theorem has been successfully implemented in a diverse range of fields, including those of science, engineering, philosophy, medicine, athletics, and the legal system. Bayesian inference is strongly related to subjective probability, which is why it is frequently referred to as "Bayesian probability" in the field of decision theory philosophy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bayesian Inference Chapter 2: Likelihood Function Chapter 3: Conjugate Prior Chapter 4: Posterior Probability Chapter 5: Maximum a Posteriori Estimation Chapter 6: Bayes Estimator Chapter 7: Bayesian Linear Regression Chapter 8: Dirichlet Distribution Chapter 9: Variational Bayesian Methods Chapter 10: Bayesian Hierarchical Modeling (II) Answering the public top questions about bayesian inference. (III) Real world examples for the usage of bayesian inference in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian inference' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian inference.
Bayesian Inference
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Author : Silvelyn Zwanzig
language : en
Publisher: CRC Press
Release Date : 2024-07-23
Bayesian Inference written by Silvelyn Zwanzig and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-23 with Mathematics categories.
Bayesian Inference: Theory, Methods, Computations provides a comprehensive coverage of the fundamentals of Bayesian inference from all important perspectives, namely theory, methods and computations. All theoretical results are presented as formal theorems, corollaries, lemmas etc., furnished with detailed proofs. The theoretical ideas are explained in simple and easily comprehensible forms, supplemented with several examples. A clear reasoning on the validity, usefulness, and pragmatic approach of the Bayesian methods is provided. A large number of examples and exercises, and solutions to all exercises, are provided to help students understand the concepts through ample practice. The book is primarily aimed at first or second semester master students, where parts of the book can also be used at Ph.D. level or by research community at large. The emphasis is on exact cases. However, to gain further insight into the core concepts, an entire chapter is dedicated to computer intensive techniques. Selected chapters and sections of the book can be used for a one-semester course on Bayesian statistics. Key Features: Explains basic ideas of Bayesian statistical inference in an easily comprehensible form Illustrates main ideas through sketches and plots Contains large number of examples and exercises Provides solutions to all exercises Includes R codes Silvelyn Zwanzig is a Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt University of Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. She has taught Statistics to undergraduate and graduate students since 1991. Her research interests include theoretical statistics and computer-intensive methods. Rauf Ahmad is Associate Professor at the Department of Statistics, Uppsala University. He did his Ph.D. at the University of Göttingen, Germany. Before joining Uppsala University, he worked at the Division of Mathematical Statistics, Department of Mathematics, Linköping University, and at Biometry Division, Swedish University of Agricultural Sciences, Uppsala. He has taught Statistics to undergraduate and graduate students since 1995. His research interests include high-dimensional inference, mathematical statistics, and U-statistics.
Bayesian Inference
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Author : Hanns L. Harney
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Bayesian Inference written by Hanns L. Harney and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Mathematics categories.
Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. The text is written at introductory level, with many examples and exercises.
Bayesian Inference
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Author : Hanns Ludwig Harney
language : en
Publisher: Springer
Release Date : 2016-10-18
Bayesian Inference written by Hanns Ludwig Harney and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-18 with Science categories.
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.