Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Statistics for Social and Behavioral Sciences (Hardback)Scott M. Lynch (author)
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This book outlines 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 real-data example in painstaking detail.
Publisher: Springer-Verlag New York Inc.
Number of pages: 359
Weight: 1590 g
Dimensions: 235 x 155 x 22 mm
Edition: 2007 ed.
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)
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