Bayesian Methods for Statistical Analysis

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. Blank Page 10 Blank Page 12 Blank Page 18 File08 Chapter 1 Bayesian Basics Part 1 Draft06d.pdf -1 1.1 Introduction 19 1.12 Bayesian interval estimation 44 Exercise 1.18 The normal-normal model 56 File09 Chapter 2 Bayesian Basics Part 2 Draft06d.pdf -1 2.1 Frequentist characteristics of Bayesian estimators 79 2.5 Bayesian decision theory 104 Exercise 2.8 Examples of the risk function and Bayes risk 106 Solution to Exercise 2.8 106 Exercise 2.9 Examples of the PEL and Bayes risk 111 Solution to Exercise 2.9 112 Exercise 2.10 Bayes estimate under the QELF 117 Solution to Exercise 2.10 117 Exercise 2.11 Bayes estimate under the AELF 118 Solution to Exercise 2.11 118 Exercise 2.12 Bayes estimate under the IELF 119 Solution to Exercise 2.12 119 . 121 File10 Chapter 3 Bayesian Basics Part 3 Draft06d.pdf -1 3.1 Inference given functions of the data 127 if((theta>=1/2)&&(theta =3/2)&&(theta =0)&&(theta =1/2)&&(theta =3/2)&&(theta