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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: TextTextSeries: Statistics for social and behavioral sciences.Publisher: New York : Springer, c2007Description: xxviii, 357 p. : ill. ; 24 cm.ISBN: 9780387712642 (hardcover : alk. paper).Subject(s): Social sciences -- Statistical methods | Bayesian statistical decision theoryDDC classification: 519.5 LYI Online 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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Item type Current location Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book EWU Library
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Non-fiction 519.5 LYI 2007 (Browse shelf) Not For Loan
Text Text EWU Library
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Non-fiction 519.5 LYI 2007 (Browse shelf) C-1 Not For Loan 25614
Text Text EWU Library
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Non-fiction 519.5 LYI 2007 (Browse shelf) C-2 Available 26049
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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.

Applied Statistics

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