As I was checking the recent stat postings on arXiv, I noticed the paper by Chen and Xie entitled inference in Kingman’s coalescent with pMCMC. (And surprisingly deposited in the machine learning subdomain.) The authors compare a pMCMC implementation for Kingman’s coalescent with importance sampling (à la Stephens & Donnelly), regular MCMC and SMC. The specifics of their pMCMC algorithm is that they simulate the coalescent times conditional on the tree structure and the tree structure conditional on the coalescent times (via SMC). The results reported in the paper consider up to five loci and agree with earlier experiments showing poor performances of MCMC algorithms (based on the LAMARC software and apparently using independent proposals). They show similar performances between importance sampling and pMCMC. While I find this application of pMCMC interesting, I wonder at the generality of the approach: when I was introduced to ABC techniques, the motivation was that importance sampling was deteriorating very quickly with the number of parameters. Here it seems the authors only considered one parameter θ. I wonder what happens when the number of parameters increases. And how pMCMC would then compare with ABC.
Archive for the University life Category
“The tone of his talks, he said, was “Let’s not talk about the plumbing, the nuts and bolts — that’s for plumbers, for statisticians.””
As I got a tablet last week and immediately subscribed to the New York Times, I started reading papers from recent editions and got to this long article of April 26, by Yudhijit Bhattacharjee on Diederik Stapel, the Dutch professor of psychology who used fake data in dozens of papers and PhD theses.
“In his early years of research — when he supposedly collected real experimental data — Stapel wrote papers laying out complicated and messy relationships between multiple variables. He soon realized that journal editors preferred simplicity.”
This article is rather puzzling in its presentation of the facts. While Stapel acknowledges making up the data that conveniently supported his theses, the journalist’s analysis is fairly ambivalent, for instance considering that faking data is a “lesser threat to the integrity of science than the massaging of data and selective reporting of experiments”. At the beginning of the article, Stapel is shown going back to places where his experiments were supposed to have taken place, but he “could not find a location that matched the conditions described in his experiment”, making it sound as if he had forgotten…
“Science is of course about discovery, about digging to discover the truth. But it is also communication, persuasion, marketing (…) People are on the road with their talk. With the same talk. It’s like a circus (…) They give a talk in Berlin, two days later they give the same talk in Amsterdam, then they go to London. They are traveling salesmen selling their story.”
The above quote from Stapel is even more puzzling, as if giving the same talk in different places is an unacceptable academic behaviour, in par with faking data and plagiarism… I do give the same talk in several conferences and seminars, mostly to different people and I do not see a problem with this. If I persist in this behaviour, it will get boring to people who see the same talk over and over, and it should lead to me not being invited to conferences or seminars any longer, but there is nothing unethical or a-scientific in this. Another illustration of the ambivalence of both the character and the article. I frankly dislike this approach to fraud, a kind of “50 shades of lies”, where all academics get under suspicion that one way or another they also acted un-ethically and in their own interest rather than towards the advancement of Science…
A strange (if very French!) debate is taking place these days in the French main chamber, where some socialist deputies are contesting an incoming change in the regulation of university studies that would allow some courses to be taught in… English! Quelle horreur!!! Since this option has been implemented by many universities, incl. Dauphine, it means that we all are acting outside the law! I do not fear in the least being indicted for teaching R and Bayesian statistics in English… However, I find the action of these deputies missing the point: just like most other Western countries, we need to attract bright students from emerging countries in order to keep our departments open. It is unrealistic to think that those students will accept to learn French in addition to English, just because our universities are that attractive (and they are not!). Plus, our own students are asking for courses in English as they realise that their English level is not that great and that this training is more efficient than regular English courses… This position was better expressed in a Le Monde tribune a few days ago signed by several university professors, incl. Cédric Villani.
Indeed, I liked the i-like workshop very much. Among the many interesting talks of the past two days (incl. Cristiano Varin’s ranking of Series B as the top influential stat. journal!) , Matti Vihola’s and Nicolas Chopin’s had the strongest impact on me (to the point of scribbling in my notebook). In a joint work with Christophe Andrieu, Matti focussed on evaluating the impact of replacing the target with an unbiased estimate in a Metropolis-Hastings algorithm. In particular, they found necessary and sufficient conditions for keeping geometric and uniform ergodicity. My question (asked by Iain Murray) was whether they had derived ways of selecting the number of terms in the unbiased estimator towards maximal efficiency. I also wonder if optimal reparameterisations can be found in this sense (since unbiased estimators remain unbiased after reparameterisation).
Nicolas’ talk was about particle Gibbs sampling, a joint paper with Sumeet Singh recently arXived. I did not catch the whole detail of their method but/as I got intrigued by a property of Marc Beaumont’s algorithm (the very same algorithm used by Matti & Christophe). Indeed, the notion is that an unbiased estimator of the target distribution can be found in missing variable settings by picking an importance sampling distribution q on those variables. This representation leads to a pseudo-target Metropolis-Hastings algorithm. In the stationary regime, there exists a way to derive an “exact” simulation from the joint posterior on (parameter,latent). All the remaining/rejected latents are then distributed from the proposal q. What I do not see is how this impacts the next MCMC move since it implies generating a new sample of latent variables. I spoke with Nicolas about this over breakfast: the explanation is that this re-generated set of latent variables can be used in the denominator of the Metropolis-Hastings acceptance probability and is validated as a Gibbs step. (Incidentally, it may be seen as a regeneration event as well.)
Furthermore, I had a terrific run in the rising sun (at 5am) all the way to Kenilworth where I was a deer, pheasants and plenty of rabbits. (As well as this sculpture that now appears to me as being a wee sexist…)