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Handbook Of Bayesian Fiducial And Frequentist Inference
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Book Synopsis Handbook of Bayesian, Fiducial, and Frequentist Inference by : James Berger
Download or read book Handbook of Bayesian, Fiducial, and Frequentist Inference written by James Berger and published by CRC Press. This book was released on 2024-02-26 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Handbook of Forensic Statistics by : David L. Banks
Download or read book Handbook of Forensic Statistics written by David L. Banks and published by CRC Press. This book was released on 2020-11-05 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Forensic Statistics is a collection of chapters by leading authorities in forensic statistics. Written for statisticians, scientists, and legal professionals having a broad range of statistical expertise, it summarizes and compares basic methods of statistical inference (frequentist, likelihoodist, and Bayesian) for trace and other evidence that links individuals to crimes, the modern history and key controversies in the field, and the psychological and legal aspects of such scientific evidence. Specific topics include uncertainty in measurements and conclusions; statistically valid statements of weight of evidence or source conclusions; admissibility and presentation of statistical findings; and the state of the art of methods (including problems and pitfalls) for collecting, analyzing, and interpreting data in such areas as forensic biology, chemistry, and pattern and impression evidence. The particular types of evidence that are discussed include DNA, latent fingerprints, firearms and toolmarks, glass, handwriting, shoeprints, and voice exemplars.
Book Synopsis Belief Functions: Theory and Applications by : Sylvie Le Hégarat-Mascle
Download or read book Belief Functions: Theory and Applications written by Sylvie Le Hégarat-Mascle and published by Springer Nature. This book was released on 2022-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.
Book Synopsis Springer Handbook of Engineering Statistics by : Hoang Pham
Download or read book Springer Handbook of Engineering Statistics written by Hoang Pham and published by Springer Nature. This book was released on 2023-04-20 with total page 1136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
Book Synopsis Handbook of Bayesian Variable Selection by : Mahlet G. Tadesse
Download or read book Handbook of Bayesian Variable Selection written by Mahlet G. Tadesse and published by CRC Press. This book was released on 2021-12-24 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material
Book Synopsis Objective Bayesian Inference by : James O Berger
Download or read book Objective Bayesian Inference written by James O Berger and published by World Scientific. This book was released on 2024-03-06 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Student’s Guide to Bayesian Statistics by : Ben Lambert
Download or read book A Student’s Guide to Bayesian Statistics written by Ben Lambert and published by SAGE. This book was released on 2018-04-20 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers.
Book Synopsis Bayesian and Frequentist Regression Methods by : Jon Wakefield
Download or read book Bayesian and Frequentist Regression Methods written by Jon Wakefield and published by Springer Science & Business Media. This book was released on 2013-01-04 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines.
Book Synopsis The Likelihood Principle by : James O. Berger
Download or read book The Likelihood Principle written by James O. Berger and published by IMS. This book was released on 1988 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inference by : Helio S. Migon
Download or read book Statistical Inference written by Helio S. Migon and published by CRC Press. This book was released on 2014-09-03 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Balanced Treatment of Bayesian and Frequentist Inference- Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition. New to the Second Edition: New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and exercises More comparison between the approaches, including their similarities and differences Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.
Book Synopsis Theory of Distances in NeutroGeometry by :
Download or read book Theory of Distances in NeutroGeometry written by and published by Infinite Study. This book was released on 2024-01-01 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: NeutroGeometry is one of the most recent approaches to geometry. In NeutroGeometry mod-els, the main condition is to satisfy an axiom, definition, property, operator and so on, that is neither entirely true nor entirely false. When one of these concepts is not satisfied at all it is called AntiGeometry. One of the problems that this new theory has had is the scarcity of models. Another open problem is the definition of angle and distance measurements within the framework of NeutroGeometry. This paper aims to introduce a general theory of distance measures in any NeutroGeometry. We also present an algorithm for distance measurement in real-life problems.
Book Synopsis Bayesian Statistical Inference by : Gudmund R. Iversen
Download or read book Bayesian Statistical Inference written by Gudmund R. Iversen and published by SAGE. This book was released on 1984-11 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statisticians now generally acknowledge the theorectical importance of Bayesian inference, if not its practical validity. According to Gudmund R. Iversen, one reason for the lag in applications is that empirical researchers have lacked a grounding in the methodology. His volume provides this introduction and serves as a companion to #4, Tests of Significance.
Book Synopsis A Comparison of the Bayesian and Frequentist Approaches to Estimation by : Francisco J. Samaniego
Download or read book A Comparison of the Bayesian and Frequentist Approaches to Estimation written by Francisco J. Samaniego and published by Springer. This book was released on 2012-09-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
Book Synopsis Bayesian Statistics for Experimental Scientists by : Richard A. Chechile
Download or read book Bayesian Statistics for Experimental Scientists written by Richard A. Chechile and published by MIT Press. This book was released on 2020-09-08 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.
Book Synopsis The Logical Foundations of Statistical Interference by : Henry Ely Kyburg
Download or read book The Logical Foundations of Statistical Interference written by Henry Ely Kyburg and published by Springer Science & Business Media. This book was released on 1974-07-31 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: When his uncle, Michael, dies of AIDS, Joel's dreams and thoughts of Michael keep his memory alive.
Book Synopsis Bayesian Data Analysis by : Andrew Gelman
Download or read book Bayesian Data Analysis written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-27 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow 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
Book Synopsis An Introductory Handbook of Bayesian Thinking by : Stephen C. Loftus
Download or read book An Introductory Handbook of Bayesian Thinking written by Stephen C. Loftus and published by Elsevier. This book was released on 2024-04-17 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: As Bayesian techniques become more common across a variety of fields, it becomes important for experts in those fields to understand those methods. An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences. Appropriate for students with some background in calculus and introductory statistics, particularly for nonstatisticians with a sufficient mathematical background, the text provides a gentle introduction to Bayesian ideas with a wide array of supporting examples from a variety of fields. Utilizes real datasets to illustrate Bayesian models and their results Guides readers on coding Bayesian models using the statistical software R, including a helpful introduction and supporting online resource Appropriate for an undergraduate statistics course, as well as for non-statisticians with sufficient mathematical background (integral and differential Calculus and an introductory Statistics course) Covers any more advanced topics which readers may not be familiar with—such as the basic idea of vectors and matrices—as much as needed in order to foster understanding of core concepts
