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Introduction to applied Bayesian statistics and estimation for social scientists / Scott M. Lynch.

By: Lynch, Scott M. (Scott Michael), 1971-Material type: TextTextLanguage: English Series: Statistics for social and behavioral sciencesPublication details: New York : Springer, c2007. Description: xxviii, 357 p. : ill. ; 24 cmISBN: 9780387712642 (hardcover : alk. paper)Subject(s): Social sciences -- Statistical methods | Bayesian statistical decision theoryDDC classification: 519.5 LYI LOC classification: HA29 | .L973 2007Online resources: Table of contents | OCLC | E-book Fulltext
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.
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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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book Dr. S. R. Lasker Library, EWU
E-book 		Non-fiction 519.5 LYI 2007 (Browse shelf(Opens below)) Not For Loan
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section 		Non-fiction 519.5 LYI 2007 (Browse shelf(Opens below)) C-1 Not For Loan 25614
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section 		Non-fiction 519.5 LYI 2007 (Browse shelf(Opens below)) C-2 Available 26049
Total holds: 0

Includes bibliographical references (p. [345]-351) and index.

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.

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.

AS

Tahur Ahmed

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