Introduction to applied Bayesian statistics and estimation for social scientists
 Responsibility
 Scott M. Lynch.
 Language
 English.
 Imprint
 New York : Springer, c2007.
 Physical description
 xxviii, 357 p. : ill. ; 24 cm.
 Series
 Statistics for social and behavioral sciences.
Access
Available online
 site.ebrary.com ebrary
SAL1&2 (oncampus shelving)
Stacks
Request
Call number  Status 

QA279.5 .L96 2007  Unknown 
More options
Creators/Contributors
 Author/Creator
 Lynch, Scott M. (Scott Michael), 1971
Contents/Summary
 Bibliography
 Includes bibliographical references (p. [345]351) and index.
 Contents

 Introduction. Probability theory and classical statistics. Basics of Bayesian statistics. Modern model estimation part 1: Gibbs sampling. Modern model estimation part 2: MetroplisHastings sampling. Evaluating MCMC algorithms and model fit. The linear regression model. Generalized linear models. Introduction to hierarchical models. Introduction to multivariate regression models. Conclusion.
 (source: Nielsen Book Data)
 Publisher's Summary
 "Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research  including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models  and it thoroughly develops each realdata example in painstaking detail. The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods  including the Gibbs sampler and the MetropolisHastings algorithm  are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2007
 Series
 Statistics for social and behavioral sciences
 Note
 "With 89 figures."
 ISBN
 9780387712642 (hbk.)
 038771264X (hbk.)
 0387712658
 9780387712659