Best N., Jackson C., Lunn D., Spiegelhalter D., Thomas A. The BUGS Book: A Practical Introduction to Bayesian Analysis

Boca Raton: CRC Press, 2012. — 339 p.The BUGS project is rooted in the idea of generic reusable components that can be put together as desired, like a child’s construction set but not quite as colourful. In this book we typically tackle each of these components one by one using deliberately simplified examples, but hopefully it will be clear that they can be easily assembled into arbitrarily complex models. This flexibility has enabled BUGS to be applied in areas that we had never dreamed about, which is gratifying. But it is also important to note that in many situations BUGS may not be the most efficient method, and there are many things it cannot do.Introduction: Probability and parameters
Monte Carlo simulations using BUGS
Introduction to Bayesian inference
Introduction to Markov chain Monte Carlo methods
Prior distributions
Regression models
Categorical data
Model checking and comparison
Issues in Modeling
Hierarchical models
Specialised models
Different implementations of BUGS
BUGS language syntax
Functions in BUGS
Distributions in BUGS