Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.

The second edition:

• Provides an integrated presentation of theory, examples, applications and computer algorithms.
• Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
• Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
• Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
• Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who 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 great source of reference for both researchers and students.

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