- Gebundene Ausgabe: 672 Seiten
- Verlag: Academic Press (25. November 2010)
- Sprache: Englisch
- ISBN-10: 0123814855
- ISBN-13: 978-0123814852
- Größe und/oder Gewicht: 19,3 x 3,3 x 23,6 cm
- Durchschnittliche Kundenbewertung: 1 Kundenrezension
- Amazon Bestseller-Rang: Nr. 118.976 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS (Englisch) Gebundene Ausgabe – 25. November 2010
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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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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.
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!
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.
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.