- 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.