Bayesian Analysis Made Simple: An Excel GUI for WinBUGS

by Phil Woodward

Overview

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 cre

Product Details

ISBN-13:	9781040173787
Publisher:	CRC Press
Publication date:	04/19/2016
Series:	Chapman & Hall/CRC Biostatistics Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	364
File size:	26 MB
Note:	This product may take a few minutes to download.

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. BugsXLA Overview and Reference Manual. Normal Linear Models (NLMs). Generalized Linear Models. Normal Linear Mixed Models. Generalized Linear Mixed Models. Emax or Four-Parameter Logistic Non-Linear Models. Bayesian Variable Selection. Longitudinal and Repeated Measures Models. Robust Models. Beyond BugsXLA: Extending the WinBUGS Code. 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: Known Issues.

What People are Saying About This

From the Publisher

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

Editorial Reviews

"…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 ultim

From the Publisher