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 Basis for, and Advantages of, Bayesian Model Estimation via Repeated Sampling.
Hierarchical Mixture 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.