Title Details

Preface.

Acknowledgements.

Introduction: The Bayesian Method, its Benefits and Implementation.
Standard Distributions: Updating, Inference and Prediction.
Models for Association and Classification.
Normal Linear Regression, General Linear Models and Log-Linear Models.
Ensemble Estimates: Hierarchical Priors for Pooling Strength.
Latent Variables, Mixture Analysis and Models for NonResponse.
Correlated Data Models.
Multilevel Models, Multivariate Analysis and Longitudinal Models.
Life Table and Survival Analysis.
Bayesian Estimation and Model Assessment.
References.
Index.

"I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and range of the discussions in contains?I can certainly recommend it..." (Short Book Reviews, Vol. 21, No. 3, December 2001)

"...aims to contribute to the development of accessible software methods for applying Bayesian methodology." (Zentralblatt MATH, Vol. 967, 2001/17)

"I would recommend this book to any industrial statistician as a good starting pint for learning about Bayesian methodology and also to those already familiar with Bayesian techniques as a helpful guide to developing proficiency in using BUGS software." (Technometrics, Vol. 44, No. 3, August 2002)

"...fills an important niche in the statistical literature and should be a vary valuable resource for students and professionals..." (Journal of Mathematical Psychology, 2002)

"...an excellent introductory book..." (Biometrics, June 2002)

"...has valuable resources for instructors, statisticians, and researchers..." (Journal of the American Statistical Association, March 2003)