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.)
Archive for Place Jussieu
MCM in Paris, 2023
Posted in Books, pictures, Statistics, Travel, University life with tags conference fees, France, MCM 2023, Monte Carlo Methods and Applications, Monte Carlo Statistical Methods, Paris, Place Jussieu, registration fees, Sorbonne Université on December 16, 2022 by xi'anN[eur]IP[ari]S [23-24 November]
Posted in pictures, Statistics, Travel, University life with tags France, Jussieu, Lutèce, mirror workshop, NeurIPS 2022, NeurIPS@Paris 2022, New Orleans, Paris, Place Jussieu, Sorbonne Université, Université Pierre et Marie Curie on October 10, 2022 by xi'an
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!
Posted in Books, Kids, pictures, Statistics, Travel, University life with tags All about that Bayes, Andrew Gelman, Bayesian Analysis, Bayesian robustness, Campus Pierre et Marie Curie, causal inference, Paris, Place Jussieu, seminar, Sorbonne Université on October 6, 2022 by xi'an
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:
- Why is it more complicated to specify the likelihood than the prior?
- 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.
Xi'an's Og 