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Applied Bayesian Modelling
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  • Wiley

More About This Title Applied Bayesian Modelling

  • English

English

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.
  • English

English

Peter Congdon is Research Professor of Quantitative Geography and Health Statistics at Queen Mary University of London. He has written three earlier books on Bayesian modelling and data analysis techniques with Wiley, and has a wide range of publications in statistical methodology and in application areas. His current interests include applications to spatial and survey data relating to health status and health service research. His recent publications include work associated with the British Historical GIS Project and international collaborative work on psychiatric admissions in London and New York.

  • English

English

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.
  • English

English

"I recommend…highly to statisticians, [and] health researchers...among others to consider keeping on their bookshelf." (Journal of Statistical Computation and Simulation, April 2005)

"…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)

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