Follow-up on my previous post on covid deaths in France

In my previous post, I compared two sources of data regarding death counts (one from SPF, Santé Publique France, for covid deaths in hospitals; one from INSEE, for all-cause deaths), in order to get a better idea of the actual death toll of covid-19 in France. In this post, I would like to do theContinue reading “Follow-up on my previous post on covid deaths in France”

A quick, preliminary study of COVID death under-reporting in France

I guess I am not the only data scientist who cannot help checking frantically how COVID data evolve daily, looking at this dashboard, or this nice visualisation. However, case counts per country are not very reliable, given that countries have very different policies regarding testing and so on; see e.g. Nate Silver’s opinion on caseContinue reading “A quick, preliminary study of COVID death under-reporting in France”

SIR models with Kermack and McKendrick

Hi! It seems about the right time to read Kermack & McKendrick, 1927, “A contribution to the Mathematical Theory of Epidemics”. It is an early article on the “Susceptible-Infected-Removed” or “SIR” model, a milestone in the mathematical modelling of infectious disease. In this blog post, I will go through the article, describe the model andContinue reading “SIR models with Kermack and McKendrick”

Remote seminars

Hi all, It seems like the current environment is perfect for the growth of remote seminars. Most of them seem to be free to attend, some of them require registration. I’ve collected some links to seminars with topics related to statistics on this page: I will try to keep the page up to datesContinue reading “Remote seminars”

Urgent call for modellers to support epidemic modelling

Hi all, As many scientists who are not usually working in epidemiology are trying to contribute to the fight against the current pandemic while getting magnets stuck in their nose,  the Royal Society has a call here:  for “modellers to support epidemic modelling” with a deadline on April 2nd (5pm British Summer Time). More detailsContinue reading “Urgent call for modellers to support epidemic modelling”

Course on Bayesian machine learning in Paris

Rémi Bardenet and I are starting a new course on Bayesian machine learning at the MVA master (Mathématiques, Vision et Apprentissage) at ENS Paris-Saclay. Details on the syllabus can be found on the MVA webpage and on this Github repository. In this post, I shortly describe what motivated us for proposing this course and provideContinue reading “Course on Bayesian machine learning in Paris”

Turning a sum of expectations into a single expectation with geometric series

At the dawn of 2020, in case anyone in the stat/ML community is not aware yet of Francis Bach’s blog started last year: this is a great place to learn about general tricks in machine learning explained with easy words. This month’s post The sum of a geometric series is all you need! shows how ubiquitousContinue reading “Turning a sum of expectations into a single expectation with geometric series”

JRSS: Series B read paper and comparison with other unbiased estimators

Hi all, The paper “unbiased Markov chain Monte Carlo with couplings” co-written with John O’Leary and Yves Atchadé has been accepted as a read paper in JRSS: Series B, to be presented on December 11 at the Royal Statistical Society. Comments can be submitted (400 words max) until two weeks after, that is December 28;Continue reading “JRSS: Series B read paper and comparison with other unbiased estimators”

BayesBag, and how to approximate it

Hi all, This post describes how unbiased MCMC can help in approximating expectations with respect to “BayesBag”, an alternative to standard posterior distributions mentioned in Peter Bühlmann‘s discussion of Big Bayes Stories (which was a special issue of Statistical Science). Essentially BayesBag is the result of “bagging” applied to “Bayesian inference”. In passing, here is an RContinue reading “BayesBag, and how to approximate it”

Coding algorithms in R for models written in Stan

Hi all, On top of recommending the excellent autobiography of Stanislaw Ulam, this post is about using the software Stan, but not directly to perform inference, instead to obtain R functions to evaluate a target’s probability density function and its gradient. With which, one can implement custom methods, while still benefiting from the great workContinue reading “Coding algorithms in R for models written in Stan”