Bayesian Methods: A Social and Behavioral Sciences Approach
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Table of contents:Background and Introduction. Likelihood Inference and the Generalized Linear Model. The Bayesian Setup. The Normal and Student's-T Models. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes Factor. Bayesian Posterior Simulation. Basics of Markov Chain Monte Carlo. Bayesian Hierarchical Models. Practical Markov Chain Monte Carlo.This is the first book to provide a comprehensive but accessible introduction to Bayesian data analysis designed specifically for those in the social and behavioral sciences. Requiring few prerequisites, it first int...
Table of contents:
Background and Introduction. Likelihood Inference and the Generalized Linear Model. The Bayesian Setup. The Normal and Student's-T Models. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes Factor. Bayesian Posterior Simulation. Basics of Markov Chain Monte Carlo. Bayesian Hierarchical Models. Practical Markov Chain Monte Carlo.
This is the first book to provide a comprehensive but accessible introduction to Bayesian data analysis designed specifically for those in the social and behavioral sciences. Requiring few prerequisites, it first introduces Bayesian statistics and inference, followed by explicit guidance on assessing model quality and model fit, and finally introduces hierarchical models within the Bayesian context, which leads naturally to Markov Chain Monte Carlo techniques and other numerical methods. The author emphasizes practical computing issues, includes specific details for Bayesian model building and testing, and uses the freely available R and BUGS software for examples and exercise problems.
Background and Introduction. Likelihood Inference and the Generalized Linear Model. The Bayesian Setup. The Normal and Student's-T Models. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes Factor. Bayesian Posterior Simulation. Basics of Markov Chain Monte Carlo. Bayesian Hierarchical Models. Practical Markov Chain Monte Carlo.
This is the first book to provide a comprehensive but accessible introduction to Bayesian data analysis designed specifically for those in the social and behavioral sciences. Requiring few prerequisites, it first introduces Bayesian statistics and inference, followed by explicit guidance on assessing model quality and model fit, and finally introduces hierarchical models within the Bayesian context, which leads naturally to Markov Chain Monte Carlo techniques and other numerical methods. The author emphasizes practical computing issues, includes specific details for Bayesian model building and testing, and uses the freely available R and BUGS software for examples and exercise problems.