Bayesian Analysis Made Simple

An Excel GUI for WinBUGS
1. Auflage 2011
ISBN: 978-1-4398-3954-6
Verlag: Chapman and Hall/CRC

Buch, Englisch, 364 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 713 g

Reihe: Chapman & Hall/CRC Biostatistics Series

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.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Woodward, Phil

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

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.

Über Autor(innen)

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.