Canberra: ANU (Australian National University) Press, 2015. — 695 p.
‘Bayesian Methods for Statistical Analysis’ is a book on statistical methods for analysing a wide variety of data. The book consists of
12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains
many exercises, all with worked
solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.
‘Bayesian Methods for Statistical Analysis’ derives from the lecture notes for a four-day course titled ‘Bayesian Methods’, which was presented to staff of the
Australian Bureau of Statistics, at ABS House in Canberra, in 2013. The book does not attempt to cover all aspects of Bayesian methods but to provide a
‘guided tour’ through the subject matter, one which naturally reflects the author's particular interests gained over years of research and teaching.
The software packages which feature in this book are
R and
WinBUGS. BUGS stands for
‘Bayesian Inference Using Gibbs Sampling’ and is a specialised software environment for the
Bayesian analysis of complex statistical models using
Markov chain Monte Carlo methods. WinBUGS, a version of BUGS for MS Windows, is available
for free at:
https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/
Abstract.
Acknowledgements.
Overview.
Bayesian Basics Part 1.
Bayesian Basics Part 2.
Bayesian Basics Part 3.
Computational Tools.
Monte Carlo Basics.
MCMC Methods Part 1.
MCMC Methods Part 2.
Inference via WinBUGS.
Bayesian Finite Population Theory.
Normal Finite Population Models.
Transformations and Other Topics.
Biased Sampling and Nonresponse.
Appendix A: Additional Exercises.
Appendix B: Distributions and Notation.
Appendix C: Abbreviations and Acronyms.
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