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Introduction to applied Bayesian statistics and estimation for social scientists

Author: Scott M Lynch
Publisher: New York : Springer, ©2007.
Series: Statistics for social science and public policy.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Lynch 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 the book is that it covers models that are most commonly used on social science research.
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Scott M Lynch
ISBN: 9780387712642 038771264X 0387712658 9780387712659
OCLC Number: 124025392
Description: xxviii, 357 pages : illustrations ; 24 cm
Contents: 1. Introduction --
2. Probability theory and classical statistics --
3. Basics of Bayesian statistics --
4. Modern model estimation part 1 : Gibbs sampling --
5. Modern model estimation part 2 : Metropolis-Hastings sampling --
6. Evaluating Markov chain Monte Carlo algorithms and model fit --
7. The linear regression model --
8. Generalized linear models --
9. Introduction to hierarchical models --
10. Introduction to multivariate regression models --
11. Conclusion --
A. Background mathematics --
B. The central limit theorem, confidence intervals, and hypothesis tests.
Series Title: Statistics for social science and public policy.
Responsibility: Scott M. Lynch.
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Abstract:

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,  Read more...

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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 Read more...

 
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