Archive for Campus Pierre et Marie Curie

All About that Bayes stroll

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , on February 9, 2024 by xi'an

For all Bayesians and sympathisers in the Paris area, an incoming All about that Bayes seminars¹ by Elisabeth Gassiat (Institut de Mathématiques d’Orsay) on 13 February, 16h00, on Campus Pierre & Marie Curie, SCAI:

A stroll through hidden Markov models

Hidden Markov models are latent variables models producing dependent sequences. I will survey recent results providing guarantees for their use in various fields such as clustering, multiple testing, nonlinear ICA or variational autoencoders.


¹Incidentally, I came across an unrelated All about that Bayes YouTube video, a talk given by Kristin Lennox (Lawrence Livermore National Laboratory). And then found out a myriad of talks or courses using that pun.

All About that Bayes restart

Posted in pictures, Statistics, University life with tags , , , , , , , , , , on September 21, 2023 by xi'an

For all Bayesians and sympathisers in the Paris area, All about that Bayes seminars are restarting this semester with a talk by Kaniav Kamari (Centrale Supélec) on 10 October, 16h00, on Campus Pierre & Marie Curie, SCAI:

Bayesian principal component analysis

The technique of principal component analysis (PCA) has recently been expressed as the maximum likelihood solution for a generative latent variable model. In this talk, I’ll first present probabilistic reformulation that is the basis for a Bayesian treatment of PCA. Then, my focus will be on showing that the effective dimensionality of the latent space (equivalent to the number of retained principal components) can be determined automatically as part of the Bayesian inference procedure.

Andrew & All about that Bayes!

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , 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:

  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.