Introduction to Bayesian Statistics

by William M. Bolstad, James M. Curran

Overview

There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective.

Product Details

ISBN-13:	9781118593226
Publisher:	Wiley
Publication date:	09/02/2016
Sold by:	JOHN WILEY & SONS
Format:	eBook
Pages:	624
File size:	11 MB
Note:	This product may take a few minutes to download.

About the Author

WILLIAM M. BOLSTAD, PhD, is a retired Senior Lecturer in the Department of Statistics at The University of Waikato, New Zealand. Dr. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting. He is author of Understanding Computational Bayesian Statistics, also published by Wiley.

JAMES M. CURRAN is a Professor of Statistics in the Department of Statistics at the University of Auckland, New Zealand. Professor Curran’s research interests include the statistical interpretation of forensic evidence, statistical computing, experimental design, and Bayesian statistics. He is the author of two other books including Introduction to Data Analysis with R for Forensic Scientists, published by Taylor and Francis through its CRC brand.

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Table of Contents

Introduction.
Scientific Data Gathering.
Displaying and Summarizing Data.
Logic, Probability, and Uncertainty.
Discrete Random Variables.
Bayesian Inference for Discrete Random Variables.
Continuous Random Variables.
Bayes' Theorem for Proportion with Continuous Prior.
Comparing Bayesian and Frequentist Inferences for Proportion.
Bayes 'Theorem for Mean of a Normal Distribution.
Comparison with Frequentist Methods for µ.
Difference Between Meas of Two Normal Distribution.
Simple Linear Regression.
Robust Bayesian Methods.
Appendix A: Introduction to Calculus.
Appendix B: Use of Statistical Tables.
Appendix C: Using the Included Minitab Macros.
Appendix D: Using the Included R Functions.
Appendix E: Short Answers to Selected Problems.
Index.

What People are Saying About This

From the Publisher

"The general tenor of this book is good and it should serve well as a text for an introductory statistics course taught from a Bayesian perspective." (Biometrics, September 2008)

"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods. (Technometrics, November 2008)

"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels.  It is a well-written book on elementary Bayesian inference, and the material is easily accessible.  It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." (Technometrics, November 2008)

"Highly recommended. Upper-division undergraduates; graduate students; professionals." (CHOICE, April 2008)

Editorial Reviews

"The general tenor of this book is good and it should serve well as a text for an introductory statistics course taught from a Bayesian perspective." (Biometrics, September 2008)

"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods. (Technometrics, November 2008)

"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels.  It is a well-written book on elementary Bayesian inference, and the material is easily accessible.  It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." (Technometrics, November 2008)

"Highly recommended. Upper-division undergraduates; graduate students; professionals." (CHOICE, April 2008)

From the Publisher