Name: Bayesian Statistics for the Social Sciences (en Inglés)
Price: 256825.00 COP
Availability: InStock
Author: David Kaplan
ISBN: 9781462553549

portada Bayesian Statistics for the Social Sciences (en Inglés)

Formato

Libro Físico

Editorial

Guilford Pubn

Autor

David Kaplan

Idioma

Inglés

N° páginas

250

Encuadernación

Tapa Dura

ISBN13

9781462553549

N° edición

0002

Bayesian Statistics for the Social Sciences (en Inglés)

David Kaplan (Autor) · Guilford Pubn · Tapa Dura

Libro Físico

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Reseña del libro "Bayesian Statistics for the Social Sciences (en Inglés)"

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.