Introduction to applied Bayesian statistics and estimation for social scientists / Scott M. Lynch.
By: Lynch, Scott M. (Scott Michael)
.
Material type: 
TextSeries: 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 | Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|---|
E-Book
|
EWU Library E-book | Non-fiction | 519.5 LYI 2007 (Browse shelf) | Not For Loan | ||||
Text
|
EWU Library Reserve Section | Non-fiction | 519.5 LYI 2007 (Browse shelf) | C-1 | Not For Loan | 25614 | ||
|
Text
|
EWU Library Circulation Section | Non-fiction | 519.5 LYI 2007 (Browse shelf) | C-2 | Available | 26049 |
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
There are no comments for this item.