Phil Woodward

Bayesian Analysis Made Simple (eBook, ePUB)

An Excel GUI for WinBUGS

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781040173787
• Artikelnr.: 72536344

Autorenporträt

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.

Inhaltsangabe

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.