Bayesian Methods: A Social and Behavioral Sciences Approach, Second Edition (Chapman Hall/CRC Statistics in the Social and Behavioral Sciences)

Gill, Jeff

ISBN 10: 1584885629 / ISBN 13: 9781584885627

Edité par Chapman and Hall/CRC, 2007

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Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks. N° de réf. du vendeur Scanned1584885629

Détails bibliographiques

Titre : Bayesian Methods: A Social and Behavioral ...

Éditeur : Chapman and Hall/CRC

Date d'édition : 2007

Reliure : Hardcover

Etat : new

Edition : 2ème Édition

A propos de ce titre

Synopsis :

Rare book

Présentation de l'éditeur:

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.