Bayesian Analysis Made Simple: An Excel GUI for WinBUGS / Edition 1 available in Hardcover
Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand.
Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do not have the time to create WinBUGS code and ancillary files for every analysis they undertake. Accessible to even those who would not routinely use Excel, this book provides a custom-made Excel GUI, immediately useful to those users who want to be able to quickly apply Bayesian methods without being distracted by computing or mathematical issues.
From simple NLMs to complex GLMMs and beyond, Bayesian Analysis Made Simple describes how to use Excel for a vast range of Bayesian models in an intuitive manner accessible to the statistically savvy user. Packed with relevant case studies, this book is for any data analyst wishing to apply Bayesian methods to analyze their data, from professional statisticians to statistically aware scientists.
About the Author
Phil Woodward was born in 1962 in Ipswich, England. After studying Statistics and Mathematics at Brunel University he joined Rolls-Royce in Derby as a statistician in their Nuclear Division. During this time he studied part-time towards a research degree in which he was introduced to the Bayesian paradigm by the late John Naylor and Sir Adrian Smith. Phil then worked for the now defunct Lucas Automotive Company, initially as the Company Statistician but also in various Quality Management roles. Since 1997 Phil Woodward has worked for Pfizer R&D in the UK. He is currently the Global Head of PharmaTherapeutics Statistics, leading the support to the research and development of new medicines from early in the discovery process up to the first studies in patients. He is the creator of the Excel GUI for WinBUGS, BugsXLA, that greatly simplifies the analysis of data using Bayesian methods. Phil is also an active member of the Royal Statistical Society: he was the 2008 Royal Statistical Society's Guy Lecturer for schools, and is a current member of the Editorial Board of its flagship magazine, Significance.
Table of Contents
Brief Introduction to Statistics, Bayesian Methods, and WinBUGS
Why Bother Using Bayesian Methods?
BugsXLA Overview and Reference Manual
Downloading and Installing BugsXLA
Bayesian Model Specification
Set Variable Types
MCMC & Output Options
Predictions and Contrasts
Graphical Feedback Interface
Normal Linear Models
Generalized Linear Models
Survival or Reliability Data
Multivariate Categorical Data
Normal Linear Mixed Models
Generalized Linear Mixed Models
Emax or Four-Parameter Logistic Non-Linear Models
Bayesian Variable Selection
Longitudinal and Repeated Measures Models
Beyond BugsXLA: Extending the WinBUGS Code
Using BugsXLA’s WinBUGS Utilities
Editing the Initial MCMC Values
Estimating Additional Quantities of Interest
Appendix A: Distributions Referenced in BugsXLA
Appendix B: BugsXLA’s Automatically Generated Initial Values
Appendix C: Explanation of WinBUGS Code Created by BugsXLA
Appendix D: Explanation of R Scripts Created by BugsXLA
Appendix E: Troubleshooting
What People are Saying About This
The author in writing this text has succeeded in making Bayesian analysis relatively simple through a graphical user interface (GUI) for WinBUGS—BugsXLA, which resides within Excel. … I recommend the book to anyone contemplating the use of Bayesian methods for the first time and already familiar with Excel for storing, summarizing and plotting basic statistical data. The text provides an ideal introduction to Bayesian approaches using Excel and ultimately will encourage the reader to migrate to WinBUGS proper.
—International Statistical Review, 80, 2012
this book will benefit … applied statisticians who are familiar with applying generalised linear models and want to consider the impact of bringing Bayesian analyses into their work. … book will help a competent statistician to run a Bayesian analysis of a generalized linear mixed model almost effortlessly.
—John Paul Gosling, Journal of Applied Statistics, 2012