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Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS (Englisch) Gebundene Ausgabe – 25. November 2010

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  • Gebundene Ausgabe: 672 Seiten
  • Verlag: Academic Press (25. November 2010)
  • Sprache: Englisch
  • ISBN-10: 0123814855
  • ISBN-13: 978-0123814852
  • Größe und/oder Gewicht: 23,6 x 19,3 x 3,3 cm
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 57.563 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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"This book is head-and-shoulders better than the others I've seen. I'm using it myself right now. Here's what's good about it: .It builds from very simple foundations. .Math is minimized. No proofs. .From start to finish, everything is demonstrated through R programs. .It helps you learn Empirical Bayesian methods from every angle."--Exploring Possibility Space blog, March 12, 2014

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1 von 1 Kunden fanden die folgende Rezension hilfreich Von Amazon Customer am 25. Mai 2014
Format: Gebundene Ausgabe Verifizierter Kauf
This book is really amazing! It teach you Bayes from scratch and in a very understandable manner, with examples related to the every-day life. I don't only like its explanatory way, but also that it contains the formulas and that everything is linked on how to make the analyses with R, which is a surplus. It is a must if you are a researcher. and if you use R
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Die hilfreichsten Kundenrezensionen auf (beta) 54 Rezensionen
114 von 115 Kunden fanden die folgende Rezension hilfreich
Best of the rest 12. Mai 2011
Von Joseph Hilbe - Veröffentlicht auf
Format: Gebundene Ausgabe
I have reviewed a number of statistics texts for academic journals over the years, and have authored published reviews of some six books specifically devoted to Bayesian analysis. I consider John Kruschke's "Doing Bayesian Data Analysis" to be the best text available for learning this branch of statistics.

Learning how to craft meaningful statistical tests and models based on Bayesian methods is not an easy task. Nor is it an easy task to write a comprehensive basic text on the subject -- one that actually guides the reader through the various Bayesian concepts and mathematical operations so that they have a solid working ability to develop their own Bayesian-based analyses.

There are now quite a few texts to choose from in this area, and some are quite good. But Kruschke's text, in my opinion, is the most useful one available. It is very well written, the concepts unique to the Bayesian approach are clearly presented, and there is an excellent instructors manual for professors who have adopted the book for their classes. Kruschke uses R and WinBUGS for showing examples of the methods he describes, and provides all of the code so that the reader can adapt the methods for their own projects.

"Doing Bayesian Data Analysis" is not just an excellent text for the classroom, but also -- and I think foremost -- it is just the text one would want to work through in order to learn how to employ Bayesian methods for oneself.

Joseph Hilbe
62 von 63 Kunden fanden die folgende Rezension hilfreich
Excellent introduction to build foundational knowledge and confidence 6. Mai 2011
Von Sitting in Seattle - Veröffentlicht auf
Format: Gebundene Ausgabe Verifizierter Kauf
I highly recommend this book to two audiences: (a) instructors looking to construct a strong course on "introduction to social science statistics" from a Bayesian perspective; and (b) social science researchers who have been educated in a classical framework and wish to learn the foundational knowledge of a Bayesian approach, without a refresher in differential calculus. (I expect it would also of interest to many physical science and engineering researchers whose methods are not highly divergent from social science (e.g., biologists, operations engineers) but I can't speak authoritatively about that.)

I'm a practicing social science researcher and have wanted for years to learn Bayesian methods deeply - I've used them in applied settings but without complete understanding. My quest to learn Bayesian methods more rigorously has been persistently stymied by texts that demand analytic solutions to prior/posterior estimation, that are excruciatingly focused on specific problems with little attention to generalization, or that skip huge areas of exposition to leap from a toy problem to a complex one with little clue of the path between them. Dr. Kruschke's text avoids all of those problems. It is remarkable for building intuition from basic principles, for avoiding page-after-page of integrals, and for having extremely clear application.

The book starts by laying out the core intuitions of Bayes's rule - instead of merely stating it (and don't we all think we know it by now?), it leads the reader through some applied examples with frequency tables. Simple? Yes; but also valuable to force oneself through. It then builds upon this knowledge systematically, going through the requisite coin toss examples - but unlike most texts, connecting them clearly to real-world examples of binomial problems. And it proceeds from there, ending up with Bayesian versions of ANOVA-type problems and logistic regression.

There are two other salient and important features of the book. First, the exercises are particularly well-chosen to reinforce the key points and demonstrate applications. I strongly recommend to work your way through them. In my case, for instance, they forced me to confront understanding of things like the "prior likelihood of the data" - a core concept that I thought I understood but really didn't until I had to solve some actual problems.

Second, the book is closely linked to the R statistics environment - surely the most popular tool used by Bayesian statisticians - and has sample programs that are illustrative, useful, and actually work. If you do Bayesian work, you're probably going to use R, and these examples will help immensely to build the set of tools you'll need.

Finally, and just to make clear, I have a disrecommendation for one audience: if you're looking for a highly mathematical treatment of Bayesian methods, it is not the right book. It is a didactic text, not a reference manual or set of derivations.

Good luck to you as a reader, and thank you to the author!
40 von 41 Kunden fanden die folgende Rezension hilfreich
Important book 7. Dezember 2011
Von Dimitri Shvorob - Veröffentlicht auf
Format: Gebundene Ausgabe
As far as I am concerned, if you write a book this good, you get to put whatever you like on the cover - puppies, Angelina Jolie, even members of the metal band "Das Kruschke". While reading "DBDA" - reading *and* stepping through the code examples - will not make you a "Bayesian black-belt", it's impressive how much information it *will* give you - the book is almost 700 pages, after all - and you don't need (but it helps) to have tried to get the hang of the "Bayesian stuff" with other books to appreciate how friendly and effective this one is. (The author's explanation of the Metropolis algorithm is a good example). At the risk of sounding grandiose, the book just might do for Bayesian methods what Apple's original Mac did for the personal computer; here's hoping.

PS. Three worthwhile related (more technical) books:

"Data analysis using regression and multilevel/hierarchical models" by Gelman and Hill. (A very nice book, like "DBDA", but intentionally not-especially-Bayesian).

"Bayesian statistical modeling" by Congdon. (A survey of Bayesian applications).

"Dynamic linear models with R" by Petris et al. Prado and West. (A nice introduction to Bayesian approach to time series).

UPDATE. There is a new kid on the block - "Bayesian modeling using WinBUGS" by Ntzoufras. Although I am still a fan of "DBDA", I think that Ntzoufras's book would be a better bet for many people. Starting with "DBDA", and moving on to that book, may be best.
26 von 27 Kunden fanden die folgende Rezension hilfreich
Amazing 3. Oktober 2011
Von R. Dunne - Veröffentlicht auf
Format: Gebundene Ausgabe
All of a sudden it just makes sense! Everyone knows that "lightbulb moment", when previously accumulated knowledge or facts become condensed into a lucid concept, where something previously opaque becomes crystal clear. This book is laden with such moments. This is the most accessible statistics text for a generation and I predict (based on prior knowledge) that it will be a major factor in moving scientists of every shape and size towards the Bayesian paradigm. Even if you're sceptical, you're likely to learn more about frequentist statistics by reading this book, than by reading any of the tomes offered by so called popularisers. If you are a social scientist, laboratory scientist, clinical researcher or triallist, this book represents the single best investment of your time. Bayesian statistics offer a single, unified and coherent approach to data analysis. If you're intimidated by the use of a scripting language like "R" or "BUGS", then don't be. The book repays your close attention and has very clear instructions on code, which elucidate the concepts and the actual mechanics of the analysis like nothing I've seen before. All in all, a great investment. The only serious question that can be raised about the design and implementation of a book such as this is: why wasn't it done before?
35 von 39 Kunden fanden die folgende Rezension hilfreich
A Much Needed Textbook! 20. Dezember 2010
Von K. Albarado - Veröffentlicht auf
Format: Gebundene Ausgabe
Finally, there's a textbook that makes Bayesian methods understandable and easy to use without requiring the reader to have an expert understanding of mathematics or programming.

The enjoyable writing style, practical explanations, and careful instruction really make Bayesian methods available to a broad audience. Kruschke has provided a much needed textbook for students of psychology and cognitive science or really anyone interested in learning how to use Bayesian methods for data analysis.

The statistical methods presented in this text are grounded within a critical discussion of experimental design providing a concrete understanding of their practical applications. An added bonus with this text is the introduction and tutorial it provides to using R and BUGS. All things considered, this textbook is an asset to any student looking to do behavioral research.
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