## 10w2170, Banff

Posted in Books, Mountains, R, Statistics with tags , , , , , , , , on September 11, 2010 by xi'an

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…)

## Off to Banff!!

Posted in Books, Mountains, R, Statistics, Travel, University life with tags , , , , , , , , , on September 10, 2010 by xi'an

Today I am travelling from Paris to Banff, via Amsterdam and Calgary, to take part in the Hierarchical Bayesian Methods in Ecology two day workshop organised at BIRS by Devin Goodsman (University of Alberta),  François Teste (University of Alberta), and myself. I am very excited both by the opportunity to meet young researchers in ecology and forestry, and by the prospect in spending a few days in the Rockies, hopefully with an opportunity to go hiking, scrambling and even climbing. (Plus the purely random crossing of Julien‘s trip in this area!) The slides will be mostly following those of the course I gave in Aosta, while using Introducing Monte Carlo Methods with R for R practicals:

Posted in Books, Statistics with tags , , , , , , , on September 10, 2009 by xi'an

I really like the models derived from capture-recapture experiments, because they encompass latent variables, hidden Markov process, Gibbs simulation, EM estimation, and hierarchical models in a simple setup with a nice side story to motivate it (at least in Ecology, in Social Sciences, those models are rather associated with sad stories like homeless, heroin addicts or prostitutes…) I was thus quite surprised to hear from many that the capture-recapture chapter in Bayesian Core was hard to understand. In a sense, I find it easier than the mixture chapter because the data is discrete and everything can [almost!] be done by hand…

Today I received an email from Cristiano about a typo in The Bayesian Choice concerning capture-recapture models:

“I’ve read the paragraph (4.3.3) in your book and I have some doubts about the proposed formula in example 4.3.3. My guess is that a typo is here, where (n-n_1) instead of n_2 should appear in the hypergeometric distribution.”

It is indeed the case! This mistake has been surviving the many revisions and reprints of the book and is also found in the French translation Le Choix Bayésien, in Example 4.19… In both cases, ${n_2 \choose n_2-n_{11}}$ should be ${n-n_1 \choose n_2-n_{11}}$, shame on me! (The mistake does not appear in Bayesian Core.)

to which I can only suggest to incorporate the error-in-variable structure, ie the possible confusion  in identifying individuals, within the model and to run a Gibbs sampler that simulates iteratively the latent variable” true numbers of individuals in captures 1 and 2″ and the parameters given those latent variables. This problem of counting the same individual twice or more has obvious applications in Ecology, when animals are only identified by watchers, as in whale sightings, and in Social Sciences, when individuals are lacking identification. [To answer specifically the overestimation question, this is clearly the case since $n_1$ and $n_2$ are larger than in truth, while $n_{11}$ presumably remains the same....]