Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

- Introduction

- Probability theory and classical statistics

- Basics of Bayesian statistics

- Modern model estimation part 1: Gibbs sampling

- Modern model estimation part 2: Metroplis-Hastings sampling

- Evaluating MCMC algorithms and model fit

- The linear regression model

- Generalized linear models

- Introduction to hierarchical models

- Introduction to multivariate regression models

- Conclusion.

From the reviews:"The book ... contains a very detailed and comprehensive description of MCMC methods useful for applied researchers. ... Undoubtedly the book is interesting ... . The reader will gain an extensive knowledge of the issues covered ... ." (Dimitris Karlis, Zentralblatt MATH, Vol. 1133 (11), 2008)"This new offering adds to our burgeoning Bayesian bookshelves a text directed at social scientists ... . To summarize, this a very useful text for a tightly bounded semester-long introduction to Bayesian statistics in the social sciences. The text is distinguished by its hands-on practical orientation which many readers will find very appealing. ... In addition, the book is handy for self-study ... ." (Jeff Gill, Journal of the American Statistical Association, Vol. 103 (483), September, 2008)"This book introduces readers to the world of Bayesian analysis and MCMC methods through brief discussions of theory, examples, and programming computations for pplications. ...The potential users of the book are students or researchers in the social sciences, or anyone that is interested in learning Bayesian techniques and MCMC methods and applying them to their practice. The book is geared... towards practical applications. ... I recommended this book to anyone who is interested in learning about Bayesian inference and MCMC methods." (Journal of Educational Measurement . Summer 2010, Vol. 47, No 2, pp. 250-254)