Statistics for social science and public policy.
Formatted Contents Note
Probability Theory and Classical Statistics Basics of Bayesian Statistics Modern Model Estimation Part 1: Gibbs Sampling Modern Model Estimation Part 2: MetroplisHastings Sampling Evaluating Markov Chain Monte Carlo Algorithms and Model Fit The Linear Regression Model Generalized Linear Models Introduction to Hierarchical Models Introduction to Multivariate Regression Models.
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.
Bibliography, etc. Note
Includes bibliographical references (pages -351) and index.
Electronic reproduction. New York : Springer, 2010. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Aug. 9, 2010). Access may be restricted to users at subscribing institutions.
9780387709598 electronic book 0387709592 electronic book 9780387712659 0387712658