Bayesian methods a social and behavioral sciences approach Second Edition by Jeff Gill 9781584885627 1584885629

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ISBN 10: 1584885629
ISBN 13: 9781584885627 
Author: Jeff Gill

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.

Bayesian methods a social and behavioral sciences approach Second Table of contents:

1. Chapter 1: Background and Introduction
2. Chapter 2: Specifying Bayesian Models
3. Chapter 3: The Normal and Student’s-t Models
4. Chapter 4: The Bayesian Linear Model
5. Chapter 5: The Bayesian Prior
6. Chapter 6: Assessing Model Quality
7. Chapter 7: Bayesian Hypothesis Testing and the Bayes Factor
8. Chapter 8: Monte Carlo and Related Methods
9. Chapter 9: Basics of Markov Chain Monte Carlo
10. Chapter 10: Bayesian Hierarchical Models
11. Chapter 11: Some Markov Chain Monte Carlo Theory
12. Chapter 12: Utilitarian Markov Chain Monte Carlo
13. Chapter 13: Advanced Makov Chain Monte Carlo

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