Applied Bayesian Modelling
· 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.
Title: Applied Bayesian Modelling
Author: Peter Congdon
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.
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