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Details
Genre/Form: | Electronic books |
---|---|
Additional Physical Format: | Printed edition: |
Material Type: | Document, Internet resource |
Document Type: | Internet Resource, Computer File |
All Authors / Contributors: |
Jean-Michel Marin; Christian P Robert |
ISBN: | 9781461486879 1461486874 1461486866 9781461486862 |
OCLC Number: | 864180801 |
Language Note: | English. |
Description: | 1 online resource (xiv, 296 pages) : illustrations (some color) |
Contents: | User's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Series -- Image Analysis. |
Series Title: | Springer texts in statistics |
Responsibility: | Jean-Michel Marin, Christian P. Robert. |
More information: |
Abstract:
Reviews
Publisher Synopsis
"The material covered is perhaps quite ambitious and covers more than an introductory course in Bayesian statistics. PhD students and all those who want to check the computational details of the Bayesian approach will find the book very useful and interesting. A lot of researchers using Bayesian approaches only through Winbugs will perhaps find this book as an excellent companion of how the methods work really and gain insight from this." (Dimitris Karlis, zbMATH 1380.62005, 2018)"This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. ... Overall this is a solid book and well worth considering by its intended audience." (David E. Booth, Technometrics, Vol. 58 (3), August, 2016)"Jean-Michel Marin's and Christian P. Robert's book Bayesian Essentials with R provides a wonderful entry to statistical modeling and Bayesian analysis. ... Overall, this is a well-written and concise book that combines theoretical ideas with a wide range of practical applications in an excellent way. Consequently, it can be highly useful to researchers who need to use Bayesian tools to analyze their datasets and professors who have to teach or students enrolled in an introductory course on Bayesian statistics." (Ana Corberan Vallet, Biometrical Journal, Vol. 58 (2), 2016) Read more...

