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”

Statistical inference on MCMC traces

Hi everyone, and Happy New Year! This post is about some statistical inferences that one can do using as “data” the output of MCMC algorithms. Consider the trace plot above. It has been generated by Metropolis–Hastings using a Normal random walk proposal, with a standard deviation “sigma”, on a certain target. Suppose that you areContinue reading “Statistical inference on MCMC traces”

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”

Estimating convergence of Markov chains

Hi all, Niloy Biswas (PhD student at Harvard) and I have recently arXived a manuscript on the assessment of MCMC convergence (using couplings!). Here I’ll describe the main result, and some experiments (that are not in the current version of the paper) revisiting a 1996 paper by Mary Kathryn Cowles and Jeff Rosenthal entitled “A simulationContinue reading “Estimating convergence of Markov chains”

Budget constrained simulations

Hi all, This post is about some results from “Bias Properties of Budget Constrained Simulations“, by Glynn & Heidelberger and published in Operations Research in 1990. I have found these results extremely useful, and our latest manuscript on unbiased MCMC recalls them in detail. Below I go through some of the results and describe the simulationsContinue reading “Budget constrained simulations”

Another update on unbiased smoothing

Hi all, This is a short update on my research on unbiased smoothing with coupled conditional particle filters. In a previous post I naively explained that I was done with the project since the article was accepted for publication in a journal. However, a bug was found in the code thanks to a very carefulContinue reading “Another update on unbiased smoothing”