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Auteur : David Kaplan
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Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.

Télécharger Bayesian Statistics for the Social Sciences (Methodology in the Social Sciences) (English Edition) de David Kaplan Francais PDF

Bayesian Statistics for the Social Sciences (Methodology ~ Bayesian Statistics for the Social Sciences (Methodology in the Social Sciences) by David Kaplan PhD PDF, ePub eBook D0wnl0ad Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems.

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Bayesian Statistics for the Social Sciences (Methodology ~ Bayesian analysis has arrived--and Kaplan has written exactly the book that social science faculty members and graduate students need in order to learn Bayesian statistics. It is sophisticated yet accessible, complete yet an easy read. This book will ride the crest of the Bayesian wave for years to come.--William R. Shadish, PhD, Department of Psychological Sciences, University of California .

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Sample Chapter: Bayesian Statistics for the Social Sciences ~ Sample Chapter: Bayesian Statistics for the Social Sciences Author: David Kaplan Subject: Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the element s of Bayesian inference, including exchangeability .

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