Applied Bayesian Modelling
Wiley Series in Probability and Statistics
1. Auflage März 2003
478 Seiten, Hardcover
Handbuch/Nachschlagewerk
Kurzbeschreibung
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.
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author's best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS - a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example - explaining fully the choice of model for each particular problem. The book
* Provides a broad and comprehensive account of applied Bayesian modelling.
* Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.
* Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.
* Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.
The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.
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.
Index.
