- 250 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Bayesian Statistics for the Social Sciences
About this book
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.
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.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Bayesian Statistics for the Social Sciences by David Kaplan in PDF and/or ePUB format, as well as other popular books in Social Sciences & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title Page
- Title Page
- Copyright
- Series Editor’s Note
- Preface to the Second Edition
- Contents
- Part I. Foundations
- Part II. Bayesian Model Building
- Part III. Advanced Topics and Methods
- List of Abbreviations and Acronyms
- References
- Author Index
- Subject Index
- About the Author