United States
£ GBP
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 BUGS 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 BUGS 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 BUGS Book's website.
Introduction: Probability and Parameters
Probability
Probability distributions
Calculating properties of probability distributions
Monte Carlo integration
Monte Carlo Simulations Using BUGS
Introduction to BUGS
DoodleBUGS
Using BUGS to simulate from distributions
Transformations of random variables
Complex calculations using Monte Carlo
Multivariate Monte Carlo analysis
Predictions with unknown parameters
Introduction to Bayesian Inference
Bayesian learning
Posterior predictive distributions
Conjugate Bayesian inference
Inference about a discrete parameter
Combinations of conjugate analyses
Bayesian and classical methods
Introduction to Markov Chain Monte Carlo Methods
Bayesian computation
Initial values
Convergence
Efficiency and accuracy
Beyond MCMC
Prior Distributions
Different purposes of priors
Vague, ‘objective’ and ‘reference’ priors
Representation of informative priors
Mixture of prior distributions
Sensitivity analysis
Regression Models
Linear regression with normal errors
Linear regression with non-normal errors
Nonlinear regression with normal errors
Multivariate responses
Generalised linear regression models
Inference on functions of parameters
Further reading
Categorical Data
2 × 2 tables
Multinomial models
Ordinal regression
Further reading
Model Checking and Comparison
Introduction
Deviance
Residuals
Predictive checks and Bayesian p-values
Model assessment by embedding in larger models
Model comparison using deviances
Bayes factors
Model uncertainty
Discussion on model comparison
Prior-data conflict
Issues in Modelling
Missing data
Prediction
Measurement error
Cutting feedback
New distributions
Censored, truncated and grouped observations
Constrained parameters
Bootstrapping
Ranking
Hierarchical Models
Exchangeability
Priors
Hierarchical regression models
Hierarchical models for variances
Redundant parameterisations
More general formulations
Checking of hierarchical models
Comparison of hierarchical models
Further resources
Specialised Models
Time-to-event data
Time series models
Spatial models
Evidence synthesis
Differential equation and pharmacokinetic models
Finite mixture and latent class models
Piecewise parametric models
Bayesian nonparametric models
Different Implementations of BUGS
Introduction BUGS engines and interfaces
Expert systems and MCMC methods
Classic BUGS
WinBUGS
OpenBUGS
JAGS
A Appendix: BUGS Language Syntax
Introduction
Distributions
Deterministic functions
Repetition
Multivariate quantities
Indexing
Data transformations
Commenting
B Appendix: Functions in BUGS
Standard functions
Trigonometric functions
Matrix algebra
Distribution utilities and model checking
Functionals and differential equations
Miscellaneous
C Appendix: Distributions in BUGS
Continuous univariate, unrestricted range
Continuous univariate, restricted to be positive
Continuous univariate, restricted to a finite interval
Continuous multivariate distributions
Discrete univariate distributions
Discrete multivariate distributions
Bibliography
Index
"If a book has ever been so much desired in the world of statistics, it is for sure this one. [...] the tens of thousands of users of WinBUGS are indebted to the leading team of the BUGS project for having eventually succeeded in finalizing the writing of this book and for making sure that the long-held expectations are not dashed. [...] it reflects very well the aims and spirit of the BUGS project and is meant to be a manual 'for anyone who would like to apply Bayesian methods to real-world problems.' [...] strikes the right distance between advanced theory and pure practice. I especially like the numerous examples given in the successive chapters which always help readers to figure out what is going on and give them new ideas to improve their BUGS skills. [...] The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. It will be the companion and reference book for all users (beginners or advanced) of the BUGS software. I have no doubt it will meet the same success as BUGS and become very soon a classic in the literature of computational Bayesian statistics."
– Jean-Louis Fouley, CHANCE, 2013
" [...] a two-in-one product that provides the reader with both a BUGS manual and a Bayesian analysis textbook, a combination that will likely appeal to many potential readers. [...] The strength of The BUGS Book is its rich collection of ambitiously constructed and thematically arranged examples, which often come with snippets of code and printouts, as well as illustrative plots and diagrams. [...] great value to many readers seeking to familiarize themselves with BUGS and its capabilities."
– Joakim Ekström, Journal of Statistical Software, January 2013
"MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS then brought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and complexity of Bayesian inference over a vast array of application areas, from finance to medicine to data mining. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the 'tricks' one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler."
– Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA
