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.
This open book is licensed under a Creative Commons License (CC BY-NC-ND). You can download Bayesian Methods for Statistical Analysis ebook for free in PDF format (8.4 MB).
Table of Contents
Bayesian Basics Part 1
Bayesian Basics Part 2
Bayesian Basics Part 3
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
Distributions and Notation
Abbreviations and Acronyms