Bayesian Analysis Made Simple

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A01=Phil Woodward
Author_Phil Woodward
auto-regressive errors
Baseline Seizure Count
Bayesian analysis
Bayesian data analysis in Excel
Biomarker Experiment
BugsXLA
BVS Approach
BVS Method
Category=PBT
Category=PS
checking
credible
Credible Intervals
default
Default Priors
Df Parameter
DIC Value
distribution
distributions
E0 Parameter
Emax Model
Emax Parameter
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
GLMM
GLMMs
hierarchical models
Hill Parameter
interval
Log Normal Error Distribution
longitudinal analysis
MCMC
MCMC Convergence
MCMC Output
MCMC Process
MCMC Sampling
model
Model Constant Parameter
posterior
Posterior Distribution
prior
Prior Distributions
priors
random effects Emax models
repeated measures statistics
robust estimation
Sample Monitor Tool
Serial Correlation
statistical modeling
variable selection techniques
WinBUGS
WinBUGS Code

Product details

  • ISBN 9781439839546
  • Weight: 830g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Aug 2011
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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.

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