Applied Bayesian Statistics

With R and OpenBUGS Examples

  • Mary Kathryn¬†Cowles

Part of the Springer Texts in Statistics book series (STS, volume 98)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Mary Kathryn Cowles
    Pages 1-11
  3. Mary Kathryn Cowles
    Pages 13-23
  4. Mary Kathryn Cowles
    Pages 49-65
  5. Mary Kathryn Cowles
    Pages 67-79
  6. Mary Kathryn Cowles
    Pages 147-177
  7. Mary Kathryn Cowles
    Pages 179-205
  8. Back Matter
    Pages 225-232

About this book

Introduction

This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs  in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results.  In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output.

Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics.  Her research areas are Bayesian and computational statistics, with application to environmental science.  She is on the faculty of Statistics at The University of Iowa.

Keywords

Bayesian Inference Multiparameter models One-parameter models OpenBUGS R WinBUGS

Authors and affiliations

  • Mary Kathryn¬†Cowles
    • 1
  1. 1.Dept. Statistics & Actuarial ScienceUniversity of IowaIowa CityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-5696-4
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-5695-7
  • Online ISBN 978-1-4614-5696-4
  • Series Print ISSN 1431-875X
  • About this book