Bayesian methods combine the evidence from the data at hand with
previous quantitative knowledge to analyse practical problems in a
wide range of areas. The calculations were previously complex, but
it is now possible to routinely apply Bayesian methods due to
advances in computing technology and the use of new sampling
methods for estimating parameters. Such developments together with
the availability of freeware such as WINBUGS and R have facilitated
a rapid growth in the use of Bayesian methods, allowing their
application in many scientific disciplines, including applied
statistics, public health research, medical science, the social
sciences and economics.
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.
Praise for the First Edition:
"It is a remarkable achievement to have carried out such a range
of analysis on such a range of data sets. I found this book
comprehensive and stimulating, and was thoroughly impressed with
both the depth and the range of the discussions it contains." - ISI
- Short Book Reviews
"This is an excellent introductory book on Bayesian modelling
techniques and data analysis" - Biometrics
"The book fills an important niche in the statistical literature
and should be a very valuable resource for students and
professionals who are utilizing Bayesian methods." - Journal of
Mathematical Psychology
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