Applied Bayesian And Classical Inference
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Author |
: F. Mosteller |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 341 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461252566 |
ISBN-13 |
: 1461252563 |
Rating |
: 4/5 (66 Downloads) |
Synopsis Applied Bayesian and Classical Inference by : F. Mosteller
The new version has two additions. First, at the suggestion of Stephen Stigler I we have replaced the Table of Contents by what he calls an Analytic Table of Contents. Following the title of each section or subsection is a description of the content of the section. This material helps the reader in several ways, for example: by giving a synopsis of the book, by explaining where the various data tables are and what they deal with, by telling what theory is described where. We did several distinct full studies for the Federalist papers as well as many minor side studies. Some or all may offer information both to the applied and the theoretical reader. We therefore try to give in this Contents more than the few cryptic words in a section heading to ~peed readers in finding what they want. Seconq, we have prepared an extra chapter dealing with authorship work published from. about 1969 to 1983. Although a chapter cannot compre hensively Gover a field where many books now appear, it can mention most ofthe book-length works and the main thread of authorship' studies published in English. We founq biblical authorship studies so extensive and com plicated that we thought it worthwhile to indicate some papers that would bring out the controversies that are taking place. We hope we have given the flavor of developments over the 15 years mentioned. We have also corrected a few typographical errors.
Author |
: F. Mosteller |
Publisher |
: |
Total Pages |
: 348 |
Release |
: 1984-11-05 |
ISBN-10 |
: 1461252571 |
ISBN-13 |
: 9781461252573 |
Rating |
: 4/5 (71 Downloads) |
Synopsis Applied Bayesian and Classical Inference by : F. Mosteller
Author |
: Frederick Mosteller |
Publisher |
: |
Total Pages |
: 303 |
Release |
: 1984-01-01 |
ISBN-10 |
: 3540909915 |
ISBN-13 |
: 9783540909910 |
Rating |
: 4/5 (15 Downloads) |
Synopsis Applied Bayesian and Classical Inference by : Frederick Mosteller
Author |
: Mary Kathryn Cowles |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 238 |
Release |
: 2013-01-04 |
ISBN-10 |
: 9781461456964 |
ISBN-13 |
: 1461456967 |
Rating |
: 4/5 (64 Downloads) |
Synopsis Applied Bayesian Statistics by : Mary Kathryn Cowles
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Kate Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
Author |
: Andrew Gelman |
Publisher |
: John Wiley & Sons |
Total Pages |
: 448 |
Release |
: 2004-09-03 |
ISBN-10 |
: 047009043X |
ISBN-13 |
: 9780470090435 |
Rating |
: 4/5 (3X Downloads) |
Synopsis Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives by : Andrew Gelman
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
Author |
: Scott M. Lynch |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 376 |
Release |
: 2007-06-30 |
ISBN-10 |
: 9780387712659 |
ISBN-13 |
: 0387712658 |
Rating |
: 4/5 (59 Downloads) |
Synopsis Introduction to Applied Bayesian Statistics and Estimation for Social Scientists by : Scott M. Lynch
This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
Author |
: Andrew Gelman |
Publisher |
: CRC Press |
Total Pages |
: 677 |
Release |
: 2013-11-01 |
ISBN-10 |
: 9781439840955 |
ISBN-13 |
: 1439840954 |
Rating |
: 4/5 (55 Downloads) |
Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
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.
Author |
: Gudmund R. Iversen |
Publisher |
: SAGE |
Total Pages |
: 88 |
Release |
: 1984-11 |
ISBN-10 |
: 0803923287 |
ISBN-13 |
: 9780803923287 |
Rating |
: 4/5 (87 Downloads) |
Synopsis Bayesian Statistical Inference by : Gudmund R. Iversen
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.
Author |
: Anthony O' Hagan |
Publisher |
: OUP Oxford |
Total Pages |
: 924 |
Release |
: 2010-03-18 |
ISBN-10 |
: 9780191613890 |
ISBN-13 |
: 0191613894 |
Rating |
: 4/5 (90 Downloads) |
Synopsis The Oxford Handbook of Applied Bayesian Analysis by : Anthony O' Hagan
Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.
Author |
: Andrew Gelman |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2004 |
ISBN-10 |
: OCLC:1409191684 |
ISBN-13 |
: |
Rating |
: 4/5 (84 Downloads) |
Synopsis Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives by : Andrew Gelman