---
product_id: 6791346
title: "Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses"
brand: "marc kéry"
price: "€ 103.92"
currency: EUR
in_stock: true
reviews_count: 7
url: https://www.desertcart.de/products/6791346-introduction-to-winbugs-for-ecologists-bayesian-approach-to-regression-anova
store_origin: DE
region: Germany
---

# Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses

**Brand:** marc kéry
**Price:** € 103.92
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- **What is this?** Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses by marc kéry
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Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses

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## Customer Reviews

### ⭐⭐⭐⭐⭐ 







  
  
    One of the most useful stats books I've ever read, for non-statisticians
  

*by S***E on Reviewed in the United States on July 21, 2013*

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!

### ⭐⭐⭐⭐ 







  
  
    Good introductory text
  

*by W***A on Reviewed in the United States on March 1, 2011*

I find this to be a very good introductory text for someone who wants to know WinBUGS, who already has a firm background in non-Bayesian statistics.  I would consider this to be an upper-level undergraduate to graduate level text.  I find that it does a very good job of getting the reader into WinBUGS analyses within a short time, and with pertinent examples.  The breadth of examples and the construction of the simulated data make this book quite appealing for someone with a stats background, but no WinBUGS experience.At the same time, however, I do have some minor issues.  First, for someone who is not familiar with R, this text requires one to concurrently learn parts of R at the same time as learning WinBUGS.  I ran into several problems while working my way through this text, and had I not already known R, I think I would have gotten quite frustrated.  So, while I truly appreciate the R-wrapper, I think a better approach would be to more-explicitly separate the R code and the WinBUGS code and focus more on the WinBUGS code.  Second, because it is a strong example-based text, there is sometimes a dearth of explanation about why certain approaches are taken.  I think some explanation of why certain priors are being used and what the alternatives are would be better.  Third, the simulations and the analyses get quite complex quite early.  I truly think more time could be spent on easier models under different designs, and that would prove more helpful than quickly ramping up to difficult models so quickly.  And finally, the supposed strength of Bayesian analyses is the ability to bring knowledge to the table with informed priors.  However, throughout this text all priors are uninformative, and the case is made about how alike these analyses are to classical analyses.  But if that's the case, then why do Bayesian?  I would have found it useful to include examples where priors are informative and the data do, and do not, reinforce the prior.  A discussion about how to create reasonable informative priors, as well as what to do in the case when the data are either non-informative or contradictory would be a useful addition to this text.

### ⭐⭐⭐⭐ 







  
  
    Practical
  

*by J***T on Reviewed in the United States on December 16, 2010*

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."

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*Last updated: 2026-03-01*