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Bayesian Data Analysis
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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 s
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Hardcover, Second Edition, 690 pages
Published
July 29th 2003
by CRC Press
(first published 1995)
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My impression on this book from what people around told me before actually reading it was that this book is the canonical textbook for those who want to get into Bayesian statistics. After having read this book from cover to cover, however, I do not think it is a good idea to start learning Bayesian statistics with this book, as it covers very wide range of topics and therefore does not get into much technical depth for most of them. I think this book is ideal for someone like me who has very ba
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The book is good in that it is rigorous, moves fairly quickly, and incorporates a lot of important stuff about robustness, computation, and so on. However, I also find that it moves a little too quickly over some of the derivations of formulas and is not sufficiently clear in its intended meaning in some places. Also the exercises at the end of the chapter do not seem to me to be answerable based on the information that occurs in the chapter. In short the information is extremely valuable but th
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This book's biggest strength is its introduction of most of the important ideas in Bayesian statistics through well-chosen examples. These are examples are not contrived: many of them came up in research by the authors over the past several years. Most examples follow a logical progression that was probably used in the original research: a simple model is fit to data; then areas of model mis-fit are sought, and a revised model is used to address them. This brings up another strength of the book:
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A canonical reference text. For a more friendly and accessible introduction, I'd recommend Statistical Rethinking: A Bayesian Course with Examples in R and Stan.
This is a challenging but rewarding book on Bayesian statistics. Before you get to any kind of computerized methods, you're going to have to get through a substantial amount of somewhat tersely presented calculus with conjugate priors; this level of rigor is both the strength and weakness of this presentation. If I could do it all again, I wouldn't read this book first - I'd read Kruschke, and then Gelman and Hill, and then come back to this book.
n.b. they're on the 3rd edition now
n.b. they're on the 3rd edition now
May 15, 2018
Bing Wang
rated it
it was amazing
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review of another edition
Shelves:
data-science-math
Skipped the last chapter.
Very detailed bayesian tutorial. Part I, II and IV are more practical for everyday's work. Should combine this text book with some practical coding exercise together. Such as pymc3 in python.
Help to organize/connect/compare many knowledge knots like mixed effect model vs hierarchical model, kriging vs gaussian process.
Very detailed bayesian tutorial. Part I, II and IV are more practical for everyday's work. Should combine this text book with some practical coding exercise together. Such as pymc3 in python.
Help to organize/connect/compare many knowledge knots like mixed effect model vs hierarchical model, kriging vs gaussian process.
This is the textbook for my Bayesian Data Analysis book. This book contains lots of real data analysis examples, and some example are repeated several times through out the book, for example a 8-school SAT score example appears in both single-parameters models and in hierarchical models. I really like how this book teaches you how to solve real problems.
More of theory and general example. It would be great to have more coding coach.
In the end I'm a bit torn. Everybody describes it as the bible of bayesian statistics and it indeed covers a wide range of topics and by that supersedes all other general textbooks on bayesianism I know of. A great amount of literature is given after each chapter for everyone who wants to learn more about specific stuff. As a first time introduction on the other hand it's an incredible bad choice - if you don't know the basics you are going to have a bad time.
Gelman has complained about sloppy notation in other books, which is weird because the notation here seems extra sloppy -- no bold or anything to differentiate between scalars and vectors, which can be a pain sometimes. But if you already have at least a tiny bit of exposure to basic Bayesian stats in the usual notation, this seems to be a good and accessible way to expand your knowledge.
classic. must have. I have owned several editions.
the definitive textbook for bayesian statistics. almost everything is here, but it's definitely not a gentle introduction.
An excellent book for bayesian analysis
challenging for a poor mathematician
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