Following the death of the mathematician Alexandre Grothendieck in 2014, his former maths department at the University of Montpellier decided to digitise in very high resolution the 28,000 pages of notes he had left to the department. Under the supervision of Jean-Michel Marin, Head of the said department! However, thanks to the French laws governing succession, those notes cannot be posted on-line without the authorisation of the five children of Grothendieck, who keep a moral right on those notes, even though they were given to the department. Grothendieck’s children want to recover all their father’s notes—which amount to more than 90,000 handwritten pages—presumably towards a bulk sale to a prestigious American university, but the succession is in limbo while the monetary value of those notes is not ascertained. And the digitised notes are stuck in this legal no man’s land as well. It is fairly ironical that those notes are at the centre of a financial conundrum, when Grothendieck’s anarchist principles led him to refuse awards and positions and to lead a recluse and frugal life in an isolated mountain village. And to prohibit the publication of those notes… Jean-Michel remains confident though that a solution can soon be reached between Grothendieck’s children, the University, the IHES, and the French National Library. I hope those notes can be made public, so that anyone could consult them. In paper or digitised format. Even though most of these pages may just be unexploitable. But at least they will be available rather than stuck in a storage for another 25 years.
Archive for Jean-Michel Marin
[A review of Bayesian Essentials that appeared in Technometrics two weeks ago, with the first author being rechristened Jean-Michael!]
“Overall this book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. One topic that I would like to see added is the use of Bayesian methods in change point problems, a topic that we found useful in a recent article and which could be added to the time series chapter. Overall this is a solid book and well worth considering by its intended audience.”
David E. BOOTH
Kent State University
When playing with Peter Rossi’s bayesm R package during a visit of Jean-Michel Marin to Paris, last week, we came up with the above Gibbs outcome. The setting is a Gaussian mixture model with three components in dimension 5 and the prior distributions are standard conjugate. In this case, with 500 observations and 5000 Gibbs iterations, the Markov chain (for one component of one mean of the mixture) has two highly distinct regimes: one that revolves around the true value of the parameter, 2.5, and one that explores a much broader area (which is associated with a much smaller value of the component weight). What we found amazing is the Gibbs ability to entertain both regimes, simultaneously.
Our book is nearly out..! The Springer webpage is ready, we have sent the proofs back, amazon
is missing has now included the above picture, things are moving towards the publication date, supposed to be November 30. Just in time for Christmas! And not too early given that we packed off in early February…
Jean-Michel Marin visited me in Paris last week and, besides taking part in Pierre’s PhD defence, we made enough progress to close two more chapters of the new edition of Bayesian Core (soon to be Bayesian Essentials with R!) This follows the good work session we had in Carnon where we also completed two chapters (although it was hard to convince anyone that renting a flat by the Mediterranean sea was at all connected with…work! While it was: the only breaks I took were my morning runs…). There just remains one single chapter to complete, now, the one on hierarchical Bayes models. By all means, I dearly want to see it done by November 1!!!