BAYESIAN STATISTICAL MODELING
* Provides an integrated presentation of theory, examples and computer algorithms
* Examines model fitting in practice using Bayesian principles
* Features a comprehensive range of methodologies and modelling techniques
* Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods
* Includes extensive applications to health and social sciences
* Features a comprehensive collection of nearly 200 worked examples
* Data examples and computer code in WinBUGS are available via ftp
Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies. Researchers in applied statistics, medical science, public health and the social sciences will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a good reference source for both researchers and students.
Table of Contents:
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.