Bayesian statistical methods have become widely used for data analysis
and modelling in recent years, and the BUGS software has become the most
popular software for Bayesian analysis worldwide. Authored by the team
that originally developed this software, The BUGS Book provides a
practical introduction to this program and its use. The text presents
complete coverage of all the functionalities of BUGS, including
prediction, missing data, model criticism, and prior sensitivity. It
also features a large number of worked examples and a wide range of
applications from various disciplines.
The book introduces regression models, techniques for criticism and
comparison, and a wide range of modelling issues before going into the
vital area of hierarchical models, one of the most common applications
of Bayesian methods. It deals with essentials of modelling without
getting bogged down in complexity. The book emphasises model criticism,
model comparison, sensitivity analysis to alternative priors, and
thoughtful choice of prior distributions--all those aspects of the "art"
of modelling that are easily overlooked in more theoretical expositions.
More pragmatic than ideological, the authors systematically work through
the large range of "tricks" that reveal the real power of the BUGS
software, for example, dealing with missing data, censoring, grouped
data, prediction, ranking, parameter constraints, and so on. Many of the
examples are biostatistical, but they do not require domain knowledge
and are generalisable to a wide range of other application areas.
Full code and data for examples, exercises, and some solutions can be
found on the book's website.
