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

By: Lynch, Scott M. (Scott Michael), 1971-Material type: Text

TextLanguage: 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 ( 3 )
Title notes ( 5 )
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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Barcode
E-Book	EWU Library E-book	Non-fiction	519.5 LYI 2007 (Browse shelf(Opens below))		Not For Loan
Text	EWU Library Reserve Section	Non-fiction	519.5 LYI 2007 (Browse shelf(Opens below))	C-1	Not For Loan	25614
Text	EWU Library 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.

Tahur Ahmed

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