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Introduction to WinBUGS for Ecologists: A Bayesian approach to regression, ANOVA, mixed models and related analyses (Englisch) Taschenbuch – 17. Juni 2010

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"I don t believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work." --The American Statistician"

"I don t believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work." --The American Statistician

"

"I don't believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work." --The American Statistician



-I don't believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work.- --The American Statistician

Über den Autor und weitere Mitwirkende

Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of Zurich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published two books with Academic Press (2010 and 2012). He has authored/coauthored 70 peer-reviewed articles and four book chapters. Since 2007, and for a total of 103 days, he has taught 23 statistical modeling workshops about the methods in the proposed book at research institutes and universities all over the world.

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Format: Taschenbuch Verifizierter Kauf
Bayes Statistik ist die Zukunft. Zu viele Argumente machen sie der klassischen p-Wert Jonglage überlegen. Wahrscheinlich tut man gut daran, sich früh damit zu beschäftigen. Texte über Bayes-Statistik sind gerne voller Formeln und lassen sich über mathematische Verteilungen aus. Wer so etwas sucht, ist hier falsch.

Dieses Buch zeigt einem den kürzsten Weg vom naturwissenschaftlichen Problem zur Tastatur und zum konkreten Lösen einfacher und weniger einfacher statistischer Probleme. Und das macht es richtig gut. Bevor man sich versieht lauft WinBUGS und man hat ein lineares Modell. Ein tolles Buch um Anfängerhürden zu überspringen. Dabei ist der Titel doppelt falsch. Es geht nicht um WinBUGS sondern auch um OpenBUGS oder JAGS und das Buch verwendet Beispiele aus der Ökologie, aber die sind alle auch für Nicht-Biologen verständlich und relevant. Inhaltlich völlig überflüssig sind die bunten Bilder von verschiedensten Tierarten, die den augedachten Beispielen zugrunde liegen - aber das lockert herrlich auf und macht wieder Mut, sich mit abstrakten Statistikproblemen zu beschäftigen.

Was muss man mitbringen? Ordentliche Vorkenntnisse in R sollte man haben, sonst versteht man nicht, wie die Beispiele konstruiert werden, und das wäre schade. Aus der klassischen Statistik sollte man mitbringen, was ein lineares Modell ist und besser wären ein paar einfache Grundkenntnisse über generalisierte lineare Modelle - Oberflächliche Kenntnisse reichen - die Vertiefung wird gut erklärt. Und natürlich Englisch - das Buch ist auf Englisch geschrieben.
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Amazon.com: 4.4 von 5 Sternen 14 Rezensionen
6 von 6 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen One of the most useful stats books I've ever read, for non-statisticians 21. Juli 2013
Von Sitting in Seattle - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
If you are an R user and a non-statistician (i.e., a professional researcher but without rigorous Math/Stats background) and want to learn Bayesian methods: get this book! It is one of the single best statistics books I've ever read for applied researchers.

Most Bayesian method books start off fine with Bayes' Rule, followed by reasonable coverage of the binomial distribution, and then plunge off the deep end with formulas for likelihood and details on how to write MCMC samplers. When I'm in a bad mood, I suspect those books reflect disdain for non-statisticians and a desire to keep Bayesian methods a secret fraternity. If you've felt the same and been stymied, and you do classical stats in R, Kery has the perfect book to learn Bayesian approaches.

Other reviewers have commented on how approachable the book is (true), how you need to know R first because it is too much heavy lifting to learn R and Bayes and WinBUGS all at the same time (I agree), and that it is light on Bayes theory and math (a very positive thing, in my opinion).

What you might also wonder is: (a) can you use it with a non-Windows computer? and (b) does it only work with WinBUGS, or could one use openbugs or JAGS instead? Answers: yes and yes. I've been working through it with JAGS running on a Mac and using the "rjags" package. The primary changes that are required involve the fact that commands to call JAGS from R (in the rjags package) are different from those to call WinBUGS.

I don't want to get too technical in a review, but the rjags approach is simple, works on Macs and Linux as well as Windows, and is not a big stretch from the book. For reference, here is rjags syntax for the first live example ("y1000" model in chapter 5.4; starting after the data setup and model file creation): "test.jags.mod <- jags.model(file="model.txt", data=test.jags.data) # no need to init in JAGS" + "test.jags.out <- coda.samples(test.jags.mod, c("pop.mean","pop.sd"), n.iter=1000) # run MCMC" + "summary(test.jags.out)" or "HPDinterval(test.jags.out)" to get the credible interval. Once you figure out a few of the key commands -- e.g., jags.model() and coda.samples() -- then it is very easy to adapt the book to JAGS. Of course, that is for someone who generally knows his or her way around R.

One surprise and extremely nice feature of the book is that Kery takes the reader painstakingly through the generalized linear model. Even after two decades of doing applied research, I learned a few things and clarified a couple of misperceptions and shaky understandings. That in itself would be a good thing, even apart from the boost in my Bayes abilities.

The main thing that could be improved is the explanation of the BUGS language. Kery goes through the glm statistical models in close detail, but spends little time on explaining the BUGS syntax and concepts, mostly teaching that by example. That's not a huge problem since it's relatively clear in context, but specific attention such as a special chapter mid-way through the book, would be welcomed.

Finally: no, you do not need to be an ecologist to benefit from it! I'm a social scientist but the models are so close (comparing groups, hierarchical models with group level covariates such as location, etc) that there is no difficulty in translation. And I'm learning a little bit about a different field which is interesting in its own right.

Is it for you? Applied researcher + using R + use linear/general linear models? Then Yes. Professional statistician? No, too simple. Want math? No, won't satisfy. Don't know R? No, go learn R first. Cheers!
7 von 7 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Practical 16. Dezember 2010
Von Jared Becksfort - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This is a great book for using WinBUGS through R with the R library R2WinBUGS. It is actually also a pretty good book for performing classical linear modeling in R. All the analyses are performed in both R and WinBUGS. Much of the data is simulated but realistic, and the author shows you how he generated the data, which is also useful. Solutions to the exercises are available at the book's web site.

There is no reason to be an ecologist to use this book. The examples translate very well to other fields.

Despite this book being very useful to me, I gave it 4 instead of 5 stars for a few reasons. Very little attempt is made to explain the theory (to be fair, he says this at the outset, and it is a book about WinBUGS, not Bayesian statistics). The expected understanding of statistics and R is somewhat uneven throughout. For example, the author in one chapter shows you how to load libraries in R and other basic housekeeping tasks, but a few chapters later he shows more advanced model specification code in R's lm function without explaining it. Expect to spend some time in the R manual if you want to understand it all. Similarly, he repeatedly says that much of the statistics behind the code is too advanced for most ecologists, which might annoy me if I were an ecologist, but then he tends to assume a lot of the theoretical statistics is already well understood by the reader.

There is a quick introduction to Generalized Linear Models which I found helpful. Basically, this is a great practical book but you will need to look elsewhere for mathematical understanding. I like Peter Hoff's "A First Course in Bayesian Statistical Methods."
5.0 von 5 Sternen Brilliant introduction to practical statistical modelling 10. September 2014
Von Ecologist - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This book is very well written and easy to read. Throughout the book there are comparisons of frequentist and Bayesian approaches to statistical modelling (simple general linear models, generalized linear models, linear and generalized linear mixed-effects models), with side-by-side examples of R and WinBUGS code. This side-by-side presentation makes the statistical concepts and the code syntax crystal clear. A brilliant short course on practical statistical modelling methods for scientists who are comfortable with basic statistical concepts and who have introductory R skills. For those coming from other GUI-based statistical software packages, consider using the R-Commander implementation of R, as there is a scripting window where code examples from this book can be implemented.
5.0 von 5 Sternen Great book for learning Bayesian statistics 11. Juni 2016
Von MOsals - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Great book for learning Bayesian statistics! Easy to read with great examples. Looking forward to buying and reading other books by this author.
5.0 von 5 Sternen Best Applied Bayesian Intro for Ecologists 9. November 2011
Von D. J. Hocking - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Kery does a remarkable job introducing the reader to applied Bayesian stats using ecological and population biology examples. The book is clearly written, the WinBUGS code is well annotated, and the examples progress from very basic to moderately sophisticated (intro hierarchical model for repeated count data and occupancy). It also has excellent tips for dealing with error messages in WinBUGS and tuning the Gibbs sampler.
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