Bayesian Inference In The Social Sciences

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Bayesian Inference in the Social Sciences

Author : Ivan Jeliazkov
Publisher : John Wiley & Sons
ISBN 13 : 1118771125
Total Pages : 352 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Bayesian Inference in the Social Sciences by : Ivan Jeliazkov

Download or read book Bayesian Inference in the Social Sciences written by Ivan Jeliazkov and published by John Wiley & Sons. This book was released on 2014-11-04 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Bayesian Analysis for the Social Sciences

Author : Simon Jackman
Publisher : John Wiley & Sons
ISBN 13 : 9780470686638
Total Pages : 598 pages
Book Rating : 4.6/5 (866 download)

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Book Synopsis Bayesian Analysis for the Social Sciences by : Simon Jackman

Download or read book Bayesian Analysis for the Social Sciences written by Simon Jackman and published by John Wiley & Sons. This book was released on 2009-10-27 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

Bayesian Statistics for the Social Sciences

Author : David Kaplan
Publisher : Guilford Publications
ISBN 13 : 1462553559
Total Pages : 275 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Bayesian Statistics for the Social Sciences by : David Kaplan

Download or read book Bayesian Statistics for the Social Sciences written by David Kaplan and published by Guilford Publications. This book was released on 2023-10-02 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples. New to This Edition *Utilizes the R interface to Stan--faster and more stable than previously available Bayesian software--for most of the applications discussed. *Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.

Bayesian Methods

Author : Jeff Gill
Publisher : CRC Press
ISBN 13 : 1584885629
Total Pages : 696 pages
Book Rating : 4.5/5 (848 download)

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Book Synopsis Bayesian Methods by : Jeff Gill

Download or read book Bayesian Methods written by Jeff Gill and published by CRC Press. This book was released on 2007-11-26 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Author : Scott M. Lynch
Publisher : Springer Science & Business Media
ISBN 13 : 0387712658
Total Pages : 376 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Introduction to Applied Bayesian Statistics and Estimation for Social Scientists by : Scott M. Lynch

Download or read book Introduction to Applied Bayesian Statistics and Estimation for Social Scientists written by Scott M. Lynch and published by Springer Science & Business Media. This book was released on 2007-06-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Statistics for Social Scientists

Author : Lawrence D. Phillips
Publisher :
ISBN 13 :
Total Pages : 474 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Bayesian Statistics for Social Scientists by : Lawrence D. Phillips

Download or read book Bayesian Statistics for Social Scientists written by Lawrence D. Phillips and published by . This book was released on 1973 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Social Inquiry and Bayesian Inference

Author : Tasha Fairfield
Publisher : Cambridge University Press
ISBN 13 : 1108382029
Total Pages : 683 pages
Book Rating : 4.1/5 (83 download)

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Book Synopsis Social Inquiry and Bayesian Inference by : Tasha Fairfield

Download or read book Social Inquiry and Bayesian Inference written by Tasha Fairfield and published by Cambridge University Press. This book was released on 2022-08-04 with total page 683 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework.

Bayesian Statistics for the Social Sciences

Author : David Kaplan
Publisher : Guilford Publications
ISBN 13 : 1462553540
Total Pages : 274 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Bayesian Statistics for the Social Sciences by : David Kaplan

Download or read book Bayesian Statistics for the Social Sciences written by David Kaplan and published by Guilford Publications. This book was released on 2023-11-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Since the publication of the first edition, Bayesian statistics is, arguably, still not the norm in the formal quantitative methods training of social scientists. Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class. This is not surprising. First, until relatively recently, it was not feasible to conduct statistical modeling from a Bayesian perspective owing to its complexity and lack of available software. Second, Bayesian statistics represents a powerful alternative to frequentist (conventional) statistics and, therefore, can be controversial, especially in the context of null hypothesis significance testing. However, over the last 20 years, or so, considerably progress has been made in the development and application of complex Bayesian statistical methods, due mostly to developments and availability of proprietary and open-source statistical software tools. And, although Bayesian statistics is not quite yet an integral part of the quantitative training of social scientists, there has been increasing interest in the application of Bayesian methods, and it is not unreasonable to say that in terms of theoretical developments and substantive applications, Bayesian statistics has arrived. Because of extensive developments in Bayesian theory and computation since the publication of the first edition of this book, there was a pressing need for a thorough update of the material to reflect new developments in Bayesian methodology and software. The basic foundations of Bayesian statistics remain more or less the same, but this second edition encompasses many new extensions"--

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Ecological Inference

Author : Gary King
Publisher : Cambridge University Press
ISBN 13 : 9780521542807
Total Pages : 436 pages
Book Rating : 4.5/5 (428 download)

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Book Synopsis Ecological Inference by : Gary King

Download or read book Ecological Inference written by Gary King and published by Cambridge University Press. This book was released on 2004-09-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.

Bayesian Statistics for Experimental Scientists

Author : Richard A. Chechile
Publisher : MIT Press
ISBN 13 : 0262044587
Total Pages : 473 pages
Book Rating : 4.2/5 (62 download)

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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. The book first covers elementary probability theory, the binomial model, the multinomial model, and methods for comparing different experimental conditions or groups. It then turns its focus to distribution-free statistics that are based on having ranked data, examining data from experimental studies and rank-based correlative methods. Each chapter includes exercises that help readers achieve a more complete understanding of the material. The book devotes considerable attention not only to the linkage of statistics to practices in experimental science but also to the theoretical foundations of statistics. Frequentist statistical practices often violate their own theoretical premises. The beauty of Bayesian statistics, readers will learn, is that it is an internally coherent system of scientific inference that can be proved from probability theory.

Bayesian Statistics for Beginners

Author : Therese M. Donovan
Publisher : Oxford University Press, USA
ISBN 13 : 0198841299
Total Pages : 430 pages
Book Rating : 4.1/5 (988 download)

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Book Synopsis Bayesian Statistics for Beginners by : Therese M. Donovan

Download or read book Bayesian Statistics for Beginners written by Therese M. Donovan and published by Oxford University Press, USA. This book was released on 2019 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.

Bayesian Methods

Author : Jeff Gill
Publisher : CRC Press
ISBN 13 : 1439862494
Total Pages : 689 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Methods by : Jeff Gill

Download or read book Bayesian Methods written by Jeff Gill and published by CRC Press. This book was released on 2014-12-11 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th

Statistical Inference

Author : Michael W. Oakes
Publisher :
ISBN 13 :
Total Pages : 204 pages
Book Rating : 4.3/5 ( download)

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Book Synopsis Statistical Inference by : Michael W. Oakes

Download or read book Statistical Inference written by Michael W. Oakes and published by . This book was released on 1990 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Author : Andrew Gelman
Publisher : John Wiley & Sons
ISBN 13 : 9780470090435
Total Pages : 448 pages
Book Rating : 4.0/5 (94 download)

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Book Synopsis Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives by : Andrew Gelman

Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Causal Inference in Statistics, Social, and Biomedical Sciences

Author : Guido W. Imbens
Publisher : Cambridge University Press
ISBN 13 : 0521885884
Total Pages : 647 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Causal Inference in Statistics, Social, and Biomedical Sciences by : Guido W. Imbens

Download or read book Causal Inference in Statistics, Social, and Biomedical Sciences written by Guido W. Imbens and published by Cambridge University Press. This book was released on 2015-04-06 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Statistical Inference as Severe Testing

Author : Deborah G. Mayo
Publisher : Cambridge University Press
ISBN 13 : 1108563309
Total Pages : 503 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.