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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.
Table of Contents
Preface
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
478 pages