# How to approach Bayesian statistical ecology in a simple way?

The Bayesian statistical analysis can be an interesting approach in ecology, population dynamics, etc. Are there any examples, tutorials or lessons that can help us learn about these tools?

## Popular Answers

Peter Christiaan Speldewinde· University of Western Australia## All Answers (6)

Susanne I Schmidt· University of Koblenz-Landau, Landau, GermanyNeil McRoberts· University of California, DavisThe book by Albert that Susanne mentioned is part of the Use R series and it is very useful as an intro to Bayesian analysis, with lots of useful R code snippets:

http://www.amazon.com/Bayesian-Computation-R-Use/dp/0387922970

Another good hands-on text book with an ecological focus is the one by Marc Kery

http://www.amazon.com/Introduction-WinBUGS-Ecologists-Bayesian-regression/dp/0123786053

If you're serious about using Bayesian methods you should get hold of WinBugs or OpenBugs. Kery's approach is to use R as an interface to access the Bugs algorithms but you can use R itself for fairly heavy duty Bayesian analysis, and WinBugs can be used without R.

Before you invest the time and effort needed to lean these programming tools though I'd spend some time reading about the background theory and philosophy of Bayesian inference and ask yourself what it is that adopting a Bayesian approach will give you that frequentist approaches won't. Howson and Urbach's book on scientific reasoning in a Bayesian framework is a good introduction, but bear in mid that these guys are pro-Bayes and openly trying to convince the reader that it's the superior approach:

http://www.amazon.com/Scientific-Reasoning-Bayesian-Colin-Howson/dp/081269578X/ref=sr_1_1?s=books&ie=UTF8&qid=1381617119&sr=1-1&keywords=Howson+Urbach+Bayesian

Hope this helps

N

Peter Christiaan Speldewinde· University of Western AustraliaAlain Ponsero· nature reserve of Saint-brieuc bayJohn Baumgartner· Macquarie UniversityI second Peter's recommendations of McCarthy 2007, and of Kery's two books. They are both easy reads and great introductions to Bayesian techniques. McCarthy's book has a couple of introductory chapters that give some background about the philosophy and logic of Bayesian methods, and compares and contrasts to frequentist methods. Kery's books present examples of ecological modelling problems, and explore them through simulation of data (in R) and subsequent model-fitting by Maximum Likelihood, and by WinBUGS (Bayesian).

As to your final question, Mick McCarthy cites E.T. Jaynes: "... Bayesian and frequentist methods often generate numerically similar answers ... However, Bayesian methods have the distinct advantage that when the numerical results differ, the Bayesian methods are invariably correct (Jaynes, 1976)."

Andrew M Gormley· Landcare Researchhttp://dx.doi.org/10.1016/j.tree.2013.10.012

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