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The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.
List of contents
Invited Papers: Statistical Models for Biological Sequence Motif Discovery.- Multi-Course Treatment Strategies for Clinical Trials of Rapidly Fatal Diseases.- Recognition of Faces versus Greebles: A Case Study in Model Selection.- Contributed Papers.
Summary
The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.
Additional text
From the reviews:
TECHNOMETRICS
"…well written and can be appreciated by those with little biology background…In general, I found many of the papers collected in this book interesting…readers who are interested in applying Bayesian analysis in real case studies may find this book useful. One might consider using this book as supplementary material in graduate-level seminar courses on Bayesian data analysis."
Journal of the American Statistical Association, June 2004
"The volume as a whole illustrates the range of models that can now be fitted following the Markov chain Monte Carlo revolution, assess the behavior of posterior summaries under model misspecifications."
Report
From the reviews:
TECHNOMETRICS
"...well written and can be appreciated by those with little biology background...In general, I found many of the papers collected in this book interesting...readers who are interested in applying Bayesian analysis in real case studies may find this book useful. One might consider using this book as supplementary material in graduate-level seminar courses on Bayesian data analysis."
Journal of the American Statistical Association, June 2004
"The volume as a whole illustrates the range of models that can now be fitted following the Markov chain Monte Carlo revolution, assess the behavior of posterior summaries under model misspecifications."
