TY - BOOK
AU - Lynch,Scott M.
TI - Introduction to applied Bayesian statistics and estimation for social scientists
T2 - Statistics for social and behavioral sciences
SN - 9780387712642 (hardcover : alk. paper)
AV - HA29 .L973 2007
U1 - 519.5 LYI 22
PY - 2007///
CY - New York
PB - Springer
KW - Social sciences
KW - Statistical methods
KW - Bayesian statistical decision theory
N1 - 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; AS
N2 - 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
UR - http://www.loc.gov/catdir/toc/fy0801/2007929729.html
UR - http://www.worldcat.org/title/introduction-to-applied-bayesian-statistics-and-estimation-for-social-scientists/oclc/124025392&referer=brief_results
UR - http://lib.ewubd.edu/ebook/7318
ER -