Case Studies in Bayesian Statistics
Editat de Constantine Gatsonis, Robert E. Kass, Alicia Carriquiry, Andrew Gelman, David Higdon, Donna K. Pauler, Isabella Verdinellien Limba Engleză Paperback – 12 aug 2002
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Specificații
ISBN-13: 9780387954721
ISBN-10: 0387954724
Pagini: 396
Ilustrații: XIV, 376 p. 18 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.6 kg
Ediția:2002
Editura: Springer
Locul publicării:New York, NY, United States
ISBN-10: 0387954724
Pagini: 396
Ilustrații: XIV, 376 p. 18 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.6 kg
Ediția:2002
Editura: Springer
Locul publicării:New York, NY, United States
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Public țintă
ResearchCuprins
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.
Recenzii
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."
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."