Applied Bayesian Modelling 1st Edition by Peter Congdon ISBN 0471486957 978-0471486954

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Product details:

ISBN 10: 0471486957

ISBN 13: 978-0471486954 

Author: Peter Congdon

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:

CHAPTER 1 Bayesian methods and Bayesian estimation
CHAPTER 2 Hierarchical models for related units
CHAPTER 3 Regression techniques
CHAPTER 4 More advanced regression techniques
CHAPTER 5 Meta-analysis and multilevel models
CHAPTER 6 Models for time series
CHAPTER 7 Analysis of panel data
CHAPTER 8 Models for spatial outcomes and geographical association
CHAPTER 9 Latent variable and structural equation models
CHAPTER 10 Survival and event history models

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Tags: Peter Congdon, Applied Bayesian, Modelling