- 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.
" a great book fills a critical gap in existing literature. It is an excellent book for anyone interested in Bayesian modeling " (Journal of the American Statistical Association, March 2005)
"It is certainly a fine choice as a supporting reference in either a first or second Bayesian methods course (Technometrics, May 2004)
"...has a contemporary feel, with recent developments in financial time series modelling and epidemiology included..." (Short Book Reviews, Vol 23(3), December 2003)