MCM in Paris, 2023

The next MCM conference takes place in (downtown) Paris next 26-30 June. Deadlines are 31 December for mini-symposia/invited sessions and 28 February for contributed talks/posters. I appreciate very much the effort in lowering the registration fees to 80€ for students and 170€ for others, whilst including lunches into the deal! (The view of Paris in the above logo is actually taking from Paris Jussieu campus.)

N[eur]IP[ari]S [23-24 November]

The Conference on Neural Information Processing Systems (abbreviated as NeurIPS) will see a mirror version in Paris this Fall, albeit in advance of the main meeting (which starts the week after on 29 November and in New Orleans, hence with a France connection!). The concept is original in that (interested) authors of accepted 2022 NeurIPS papers will present their paper on 23-24 November. Registration is free. But attendance is not guaranteed if the number of registrations exceed the site capacity, in which case attendees will be selected at random.

Andrew & All about that Bayes!

Andrew Gelman is giving a talk on 11 October at 2 p.m. in Campus Pierre et Marie Curie (Sorbonne Université), room 16-26-209. He will talk about

Prior distribution for causal inference

In Bayesian inference, we must specify a model for the data (a likelihood) and a model for parameters (a prior). Consider two questions:

1. Why is it more complicated to specify the likelihood than the prior?
2. In order to specify the prior, how could can we switch between the theoretical literature (invariance, normality assumption, …) and the applied literature (experts elicitation, robustness, …)?

I will discuss those question in the domain of causal inference: prior distributions for causal effects, coefficients of regression and the other parameters in causal models.

BASICS workshop in Paris [29-30/09]

There will be a workshop on Bayesian non-parametrics, deep learning and uncertainty quantification, marking the closure of the BASICS ANR project, at Paris Sorbonne University, on campus Pierre et Marie Curie, on 29-30 September, with many friends speaking there. The participation is free. Registration is, however, compulsory and now open.

a stretched view on Keynes’ Treatise

I came across a rather bemusing interpretation of Keynes’ Treatise on Probability, as a tribune in Le Monde of 6 September, as being a statement against the mathematical modelling of economy. Written by Annie Cot, professor of economics at Paris Sorbonne University. While the philosophical thread of the book is inclined towards a subjective perception of probability, albeit rejecting the Bayesian approach, and while the view on statistics is equally pessimistic, falling into the infinite regress of conditioning on the observation itself, outside a Bayesian framework, as I discussed in my 2011 paper, the book makes no mention whatsoever of economics or economic models. As far as I remember the book from reading it ten years ago. To conclude, as the author of this tribune, that Keynes rejected the viability of prevision based on economic models via this book sounds therefore stretching the facts to a fair extent.