Bayesian methods for statistical analysis / by Borek Puza.

Format:

Book

Author/Creator:

Puza, Borek, author.

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Physical Description:

1 online resource (xvii, 679 pages) : illustrations, charts; digital, PDF file(s).

Edition:

1st ed.

Place of Publication:

ANU Press 2015

Australia : Australian National University, 2015.

Language Note:

English

Summary:

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.

Contents:

Intro

Abstract

Acknowledgements

Preface

Overview

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

Bibliography.

Notes:

Includes bibliographical references.

CC BY-NC-ND

Description based on online resource; title from PDF title page (ebrary, viewed October 13, 2017).

ISBN:

9781921934261

1921934263

OCLC:

946217739

Publisher Number:

10.26530/OAPEN_611011

Access Restriction:

Open access Unrestricted online access