Site icon R-bloggers

10w2170, Banff

[This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Yesterday night, we started the  Hierarchical Bayesian Methods in Ecology workshop by trading stories. Everyone involved in the programme discussed his/her favourite dataset and corresponding expectations from the course. I found the exchange most interesting, like the one we had two years ago in Gran Paradiso, because of the diversity of approaches to Statistics reflected by the exposition. However, a constant theme is the desire to compare and rank models (this term having different meanings for different students) and the understanding that hierarchical models are a superior way to handle heterogeneity and to gather strength from the whole dataset. A two-day workshop is certainly too short to meet students’ expectations and I hope I will manage to focus on the concepts rather than on the maths and computations…

As each time I come here, the efficiency of BIRS in handling the workshop and making everything smooth and running amazes me. Except for the library, I think it really compares with Oberwolfach in terms of environment and working facilities. (Oberwolfach offers the appeal of seclusion and the Black Forest, while BIRS is providing summits all around plus the range of facility of the Banff Centre and the occasional excitement of a bear crossing the campus or a cougar killing a deer on its outskirt…)


Filed under: Books, Mountains, R, Statistics Tagged: Banff, BIRS, Ecology, forestry, Gran Paradiso, hierarchical Bayesian modelling, Oberwolfach, statistical modelling, University of Alberta

To leave a comment for the author, please follow the link and comment on their blog: Xi'an's Og » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.