Applied Bayesian Statistics: With R and OpenBUGS Examples / Edition 1

by Mary Kathryn Cowles

Overview

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.

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.

Product Details

ISBN-13:	9781461456957
Publisher:	Springer New York
Publication date:	01/03/2013
Series:	Springer Texts in Statistics , #98
Edition description:	2013
Pages:	232
Product dimensions:	9.20(w) x 6.30(h) x 0.80(d)

About the Author

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.

Table of Contents

What is Bayesian statistics?.- Review of probability.- Introduction to one-parameter models.- Inference for a population proportion.- Special considerations in Bayesian inference.- Other one-parameter models and their conjugate priors.- More realism please: Introduction to multiparameter models.- Fitting more complex Bayesian models: Markov chain Monte Carlo.- Hierarchical models, and more on convergence assessment.- Regression and hierarchical regression models.- Model Comparison, Model Checking, and Hypothesis Testing.- References.- Index.

Editorial Reviews

From the book reviews:

“Tise textbook is based on the author’s course ‘Bayesian statistics’ and thus it is organised in an incremental manner that, using a variety of practical examples, guides the readers, students and researchers, through the concepts and methodologies required to perform Bayesian analysis. … Each chapter contains examples that underline the important theoretical concepts that are presented and concludes with a selection of problems and exercises. … this book is equally valuable to researchers and lecturers who wish to know more about Bayesian inference.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)

“This book … provides a gentle introduction to both the theory and the ‘nuts and bolts’ of Bayesian analysis. … For those (students in particular) who are looking for a friendly introduction to what is becoming a more popular statistical approach in many areas of science, Applied Bayesian Statistics: With R and OpenBUGS Examples is a very appropriate starting point, one that will give the reader enough understanding and experience to move on to more advanced treatments … .” (Nicole Lazar, Technometrics, Vol. 55 (4), November, 2013)

From the Publisher