Loup du Pic

yet another conference scam

Within a few days of the online appearance of the programme of the Workshop on Functional Inference and Machine Intelligence (FIMI) at the University of Bristol, which I am attending, I received the following email about accommodation, which did not sound right despite the focus on the right conference at the right time. After contacting the organisers, it proved to be a scam. A similar thing happened a few months ago with our workshop at CIRM, when one participant was asked to pay for an hotel room that happened to be 100km away… Again taking advantage of a published list of participants to the workshop. There as well the participant found the email suspicious and contacted us.

From: BOOKINGS GTE <bookings@g-travelexperts.com>
To: christian.robert@ceremade.dauphine.fr
Subject: Accommodation in Bristol, England - March 25-27, 
2024 - University of Bristol

Dear Christian,

For your upcoming conference visit to Bristol in March.

Kindly provide your arrival and departure dates for us 
to finalize your reservation. Once confirmed, we will 
email you an invoice and an itinerary including detailed 
information about your booking. Your booking is fully flexible 
for alterations, modifications, or complete cancellations 
up to 14 days before your scheduled check-in date.

Thanks,

Contact the Global Travel Experts Team via our Call Centres:
Our team is ready to assist.

Natural statistical science [#2]

A rare occurrence of a Bayesian statistics paper in Nature with this “State estimation of a physical system with unknown governing equations” by Course and Nair. A variational Bayes modelling of a state system observed with noise, but without a physical model on the state (SDE) evolution itself. Which means a prior is set on a non-parametric or neural representation of the drift and a linear approximation is used for the variational approximation, leading to a Gaussian process as the approximate distribution. While this applies to highly complex models, like orbiting black holes, it is somewhat a surprise to meet this application of variational inference in a prestigious general science journal like Nature. (The picture above was taken on the train from Marseille at the end of the Bayes Fall school.)

“The approach is based on a technique called Bayesian inference, which is used widely, but which can be computationally challenging for complex systems.” B. Keith

off to Luminy [Autumn school in Bayesian statistics]

insufficient Gibbs sampling

We have just arXived our paper on insufficient Gibbs sampling with Antoine Luciano and Robin Ryder, from Université Paris Dauphine. This is Antoine’s first paper and part of his PhD. (In particular, he wrote the entire code.) The idea stemmed from a discussion on ABC benchmarks, like the one when the pair (median, MAD) is the only available observation. With no available joint density, the setting seems to prohibit calling for an MCMC sampler. However, simulating the complete data set conditional on these statistics proves feasible, with a bit of bookkeeping. With obviously much better results [demonstrated above for a Cauchy example] than when calling ABC and at a very similar cost. (If not accounting for the ability of ABC to be parallelised.)  The idea can be extended to other settings, obviously, as long as completion remains achievable. (And a big thanks to our friend Ed George who suggested the title, while at CIRM. I had suggested “Gibbs for boars” as a poster title, in connection with the historical time-line of

Gibbs for Kids (Casella and George) — Gibbs for Pigs (Gianola) — Gibbs for Robust Pigs = Gibbs for Boars

and the abundance of boars on the Luminy campus, but this did not sound convincing enough for Antoine.)