Bayesian Statistical Modelling (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

Bayesian methods draw upon previous research findings and combine them with sample data to analyze problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modeling presents an accessible overview of modeling applications from a Bayesian perspective. 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 modeling techniques. Covers recent innovations in Bayesian modeling, 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 modeling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modeling areas. The book provides a wide ranging review of recent Bayesian modeling 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.