Applied Bayesian Modelling

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 use of Bayesian statistics has grown significantly in recentyears, and will undoubtedly continue to do so. Applied BayesianModelling is the follow-up to the author's best sellingbook, Bayesian Statistical Modelling, and focuses on thepotential applications of Bayesian techniques in a wide range ofimportant topics in the social and health sciences. Theapplications are illustrated through many real-life examples andsoftware implementation in WINBUGS - a popular softwarepackage that offers a simplified and flexible approach tostatistical modelling. The book gives detailed explanations foreach example - explaining fully the choice of model for eachparticular problem. The book· Provides a broad and comprehensive account of appliedBayesian modelling.
· Describes a variety of model assessment methods and theflexibility of Bayesian prior specifications.
· Covers many application areas, including panel datamodels, structural equation and other multivariate structuremodels, spatial analysis, survival analysis and epidemiology.
· Provides detailed worked examples in WINBUGS toillustrate 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 anddata analysis for a wide range of people involved in appliedstatistical analysis, including researchers and students fromstatistics, and the health and social sciences. The wealth ofexamples makes this book an ideal reference for anyone involved instatistical modelling and analysis.

Preface.
The Basis for, and Advantages of, Bayesian Model Estimation viaRepeated 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: EpidemiologicalMethods and Models.
Index.