CIRM, Luminy, 1995

StatMathAppli, Fréjus, France [18-22 September 2023]

The bi-yearly StatMathAppli conference will take place next September in Fréjus, France, with guest lecturers Marco Cuturi (ENSAE & Apple ML Research) who will a lecture on “Optimal Transport: From Theory to Tweaks, Computations and Applications in Machine Learning”, and Gareth O. Roberts (University of Warwick) who will give a lecture on “Topics in Retrospective Simulation”. Registration is open and the number of places is limited. (I got invited to the 2002 edition of this workshop, which took place in CIRM, Luminy.)

Fusion at CIRM

Today is the first day of the FUSION workshop Rémi Bardenet and myself organised. Due to schedule clashes, I will alas not be there, since [no alas!] at the BNP conference in Chili. The program and collection of participants is quite exciting and I hope more fusion will result from this meeting. Enjoy! (And beware of boars, cold water, and cliffs!!!)

Bayesian Methods for the Social Sciences [and for the Parisians]

[Reposting the announcement of a Bayesian conference in Paris, next October, with a fabulous list of friends speakers! Note that this is the week prior to our Fusion workshop at CIRM, Marseille. And to BNP.]

This three-day workshop will gather statisticians, mathematicians and social scientists around the theme of Bayesian statistical methods for the social sciences. This area has been growing rapidly in the past decade, and the speakers will include some of the leading researchers in the area from around the World.

The first day will consist of tutorial introductions to Bayesian inference, demography and social network analysis. Days 2 and 3 will consist of talks and posters on cutting-edge research in the area. The workshop is sponsored by the Fondation des Sciences Mathématiques de Paris (FSMP), and the tutorial sessions are organised jointly with the French Institute of Mathematics for Planet Earth.

It will be held in person at the Institut Henri Poincaré in Paris from October 19 to 21, 2022.

Kick-Kac teleportation

Randal Douc, Alain Durmus, Aurélien Enfroy, and Jimmy Olson have arXived their Kick-Kac teleportation paper, which was presented by Randal at CIRM last semester. It is based on Kac’s theorem, which states that, for a Markov chain with invariant distribution π, under (π) stationarity, the average tour between two visits to an accessible set C is also stationary. Which can be used for approximating π(h) if π(C) is known (or well-estimated). Jim Hobert and I exploited this theorem in our 2004 perfect sampling paper. The current paper contains a novel proof of the theorem under weaker conditions. (Note that the only condition on C is that it is accessible, rather than a small set. Which becomes necessary for geometric ergodicity, see condition (A4).)

What they define as the Kick-Kac teleportation (KKT) process is the collection of trajectories between two visits to C. Their memoryless version requires perfect simulations from π restricted to the set C. With a natural extension based on a Markov kernel keeping π restricted to the set C stationary. And a further generalisation allowing for lighter tails that also contains the 2005 paper by Brockwell and Kadane as a special case.

The ability of generating from a different kernel Q at each visit to C allows for different dynamics (as in other composite kernels). In their illustrations, the authors use lowest density regions for C, which is rather surprising to me. Except that it allows for a better connection between modes of the target π: the higher performances of the KKT algorithms against the considered alternatives are apparently dependent on the ability of the kernel Q to explore other modes with sufficient frequency.