Introduction to Applied Bayesian Statistics and Estimation for SocialScientists covers the complete process of Bayesian statistical analysis ingreat detail from... » 

Introduction to Applied Bayesian Statistics and Estimation for SocialScientists covers the complete process of Bayesian statistical analysis ingreat detail from the development of a model through the process of makingstatistical inference. The key feature of this book is that it covers modelsthat are most commonly used in social science research including the linearregression model generalized linear models hierarchical models andmultivariate regression models and it thoroughly develops each realdataexample in painstaking detail. The first part of the book provides a detailedintroduction to mathematical statistics and the Bayesian approach tostatistics as well as a thorough explanation of the rationale for usingsimulation methods to construct summaries of posterior distributions. Markovchain Monte Carlo MCMC methods including the Gibbs sampler and theMetropolisHastings algorithm are then introduced as general methods forsimulating samples from distributions. Extensive discussion of programming MCMCalgorithms monitoring their performance and improving them is provided beforeturning to the larger examples involving real social science models and data.TOCIntroduction. Probability theory and classical statistics. Basics ofBayesian statistics. Modern model estimation part 1 Gibbs sampling. Modernmodel estimation part 2 MetroplisHastings sampling. Evaluating MCMCalgorithms and model fit. The linear regression model. Generalized linearmodels. Introduction to hierarchical models. Introduction to multivariateregression models. Conclusion. «