Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) (英語) ハードカバー – 2003/7/29
Kindle 端末は必要ありません。無料 Kindle アプリのいずれかをダウンロードすると、スマートフォン、タブレットPCで Kindle 本をお読みいただけます。
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:
- Stronger focus on MCMC
- Revision of the computational advice in Part III
- New chapters on nonlinear models and decision analysis
- Several additional applied examples from the authors' recent research
- Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more
- Reorganization of chapters 6 and 7 on model checking and data collection
Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems." -John Grego, University of South Carolina "Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods" -Prof. David Blackwell, Department of Statistics, University of California, Berkeley Praise for the first edition: "A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life." -Robert Matthews, Aston University, in New Scientist "an essential reference text for any applied statistician" -Stephen Brooks, University of Cambridge, in The Statistician "will contribute to closing the gap between scientists and statisticians" -Sander Greenland, UCLA, in American Journal of Epidemiology "an excellent teaching reference for advanced undergraduate and graduate courses" -Nicky Best, Imperial College School of Medicine, in Statistics in Medicine
Gelman's book is the first book I've read that strikes a balance between the formulation and the explanation.
This book is not for those looking for the theoretical motivation behind Bayesian analysis, or those interested in absorbing the bounds of asymptotic performance, etc. Christian Robert's "The Bayesian Choice", or his other co-authored books, is a much better place for those who have already gotten their minds around Bayesian statistics and want to explore the gory details.
I don't dock Gelman's book for the limited amount of formal propositions/theorems/proofs because I feel that there are plenty of other decent books that do that well. But Gelman's book fills a much-needed gap for those interesting in starting out in Bayesian statistics.
I've found that an excellent supplement is "Bayesian Modeling Using WinBugs" by Ntzoufras. Gelman et al. provide wordy but enlightening explanations of Bayesian concepts with just the right amount of Math for someone that wants get their hands dirty and analyze some data with competance. The book is full examples with nice discussions from someone with a deep understanding of statistical inference. What these examples sometime lack is details of how the results were got.
This is where Ntzoufras comes in.
Ntzoufras complements Gelman perfectly by offering a book full of detailed examples with a lot of R and WinBugs code.
Jim Albert's book, "Bayesian Computation with R" is also a very good supplement to Part III of Gelman et al. as is Albert's LearnBayes R package.
Gelman has also co-written an R package called "arms" which can also supplement some of this book.
The authors do a good job of building up from simple models to ones with more and more generalization. They also do a good job of adding in real life data sets, and walking you through how they modeled the data sets, verified the results, sampled various posterior distributions, etc.
The one aspect of the book that I found a little unbalanced was that it was wordier than mathematical/coded. In one sense that's a virtue of the authors, many mathematics books drill you with hundreds of pages of dense math, and I'm sure by avoiding that path, the book's audience is larger. But I still found myself, particularly when first being introduced to a concept, wanting to see an explicit simple calculation, just to make sure that I fully understood the basic concept.
Along the same lines of the past paragraph, I found Albert's 'Bayesian Computation with R' to be a good supplement. It is rich in code, and thin in text, so the two books balance each other well. I typically found myself reading a chapter in this text, then finding the associated chapter in Albert's book, then coding up some additional examples on my own.
All and all good stuff. I'd give it 4.5 stars, and will flip a coin to see if that ends up being recorded as a 4 or 5! tails it was...
Topcis about hierarcical models and MonteCarlo markov Chain method are explained clearly.
I think that a minus prerequisite is a good knowledge of classical stastical inference, stastical models and software packaging as R, stata or Win Bugs.