Chapman and Hall/CRC
342 pages | 222 B/W Illus.
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.
"…excellent … for learning or applying [the Bayesian approach]. … wonderful case studies. Researchers and graduate students should read and familiarize with the software to practice Bayesian concepts."
—Journal of Statistical Computation and Simulation, Vol. 84, 2014
"One of its strongest features is the case studies for the different models. … the book clearly and accurately describes how to use BugsXLA. Each model and feature is illustrated with at least one case study. … this book and the Excel add-in BugsXLA make a nice contribution to the Bayesian ecosystem. They service the niche of applied researchers who want to join the Bayesian parade but find the learning curve of WinBUGS too steep to march up"
—Peter Lenk, The American Statistician, November 2013
"I think that the major strength of this book is the case studies section. The author uses a problem-solving approach, illustrating the methodology by focusing on the aims of the analysis. … The author’s style is rich but clear and comprehensible even for non-technical readers. … I would recommend this book to statisticians who already know Bayesian theory and MCMC algorithms and understand the use of specific statistical software like WinBUGS or R. For these readers, the book is an excellent manual and a precious source of suggestions to quickly and efficiently implement complex Bayesian linear models in Excel."
—Journal of Agricultural, Biological, and Environmental Statistics, Volume 19, Number 1, 2013
"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
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