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.

English.

Includes bibliographical references.

1. Bayesian basics part 1 -- 2. Bayesian basics part 2 -- 3. Bayesian basics part 3 -- 4. Computational tools -- 5. Monte Carlo basics -- 6. MCMC methods part 1 -- 7. MCMC methods part 2 -- 8. Inference via WinBUGS -- 9. Bayesian finite population theory -- 10. Normal finite population models -- 11. Transformations and other topics -- 12. Biased sampling and nonresponse -- Appendix A: Additional exercises -- Appendix B: Distributions and notation -- Appendix C: Abbreviations and acronyms.

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