Congdon, Peter Applied Bayesian Modelling Wiley Series in Probability and Statistics
1. Edition - March 2003 97.90 Euro 2003. 478 Pages, Hardcover - Handbook/Reference Book - ISBN-10: 0-471-48695-7 ISBN-13: 978-0-471-48695-4 - John Wiley & Sons
Short description Bayesian statistics uses information from past experience to infer the results of future events. With recent advances in computing power and the development of computer intensive methods for statistical estimation, Bayesian approaches to model estimation have become more feasible and popular. Congdon focuses on the potential of Bayesian techniques for modeling applications in a wide range of areas that are important in the social and health sciences. He illustrates the practical applications of advanced Bayesian modelling with software cues by integrating real-life examples using the WINBUGS software.
From the contents Preface.
The Basis for, and Advantages of, Bayesian Model Estimation via Repeated Sampling.
Hierarchical Mixture Models.
Regression Models.
Analysis of Multi-Level Data.
Models for Time Series.
Analysis of Panel Data.
Models for Spatial Outcomes and Geographical Association.
Structural Equation and Latent Variable Models.
Survival and Event History Models.
Modelling and Establishing Causal Relations: Epidemiological Methods and Models.
