Bayesian Methods for Statistical Analysis

by Borek Puza

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Book Description

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

Chapter 1
Bayesian Basics Part 1
Chapter 2
Bayesian Basics Part 2
Chapter 3
Bayesian Basics Part 3
Chapter 4
Computational Tools
Chapter 5
Monte Carlo Basics
Chapter 6
MCMC Methods Part 1
Chapter 7
MCMC Methods Part 2
Chapter 8
Inference via WinBUGS
Chapter 9
Bayesian Finite Population Theory
Chapter 10
Normal Finite Population Models
Chapter 11
Transformations and Other Topics
Chapter 12
Biased Sampling and Nonresponse
Appendix A
Additional Exercises
Appendix B
Distributions and Notation
Appendix C
Abbreviations and Acronyms

Book Details

ANU Press
ISBN13 Digital
ISBN10 Digital
PDF Size
8.4 MB

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