Lynch, Scott M. 1971-

Introduction to applied Bayesian statistics and estimation for social scientists / Scott M. Lynch. - New York : Springer, c2007. - xxviii, 357 p. : ill. ; 24 cm. - Statistics for social and behavioral sciences .

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.



9780387712642 (hardcover : alk. paper)

2007929729


Social sciences--Statistical methods.
Bayesian statistical decision theory.

HA29 / .L973 2007

519.5 LYI / 2007

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