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.Contents:Preface 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 Modelling Hierarchical models Specialised models Different implementations of BUGS BUGS language syntax Functions in BUGS Distributions in BUGS Bibliography
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