Adrien Corenflos
@adriencorenflos.bsky.social
Assistant Professor at the university of Warwick.
I compute integrals for a living.
https://adriencorenflos.github.io/
I compute integrals for a living.
https://adriencorenflos.github.io/
We believe there are many nice and important consequences to the construction. One we find particularly nice is that it gives diagnostics of convergence for MCMC algorithms in *any* f-divergence, e.g. the total variation. This is because we essentially have f-div ≤ Σₙ f(2N Wⁿ) for "correct" weights.
October 20, 2025 at 4:10 PM
We believe there are many nice and important consequences to the construction. One we find particularly nice is that it gives diagnostics of convergence for MCMC algorithms in *any* f-divergence, e.g. the total variation. This is because we essentially have f-div ≤ Σₙ f(2N Wⁿ) for "correct" weights.
This is what the original illustration showed. You make pairs, wait until they meet, at which point you equalise their weights: both particles are one and the same, so it doesn't matter! Once that's done, you just need to swap them (shuffle) with the other pairs that met, and the cycle continues.
October 20, 2025 at 4:10 PM
This is what the original illustration showed. You make pairs, wait until they meet, at which point you equalise their weights: both particles are one and the same, so it doesn't matter! Once that's done, you just need to swap them (shuffle) with the other pairs that met, and the cycle continues.
The idea is to make several Markov chains interact and exchange information via their weights. To do so we rely on *couplings* of chains X₀, X₁, ..., Xₙ, which are computable joint distributions the chains satifying P(Xₙ = Yₙ | Xₙ₋₁, Yₙ₋₁) > 0. sites.google.com/site/pierrej...
October 20, 2025 at 4:10 PM
The idea is to make several Markov chains interact and exchange information via their weights. To do so we rely on *couplings* of chains X₀, X₁, ..., Xₙ, which are computable joint distributions the chains satifying P(Xₙ = Yₙ | Xₙ₋₁, Yₙ₋₁) > 0. sites.google.com/site/pierrej...
A little self promotion: Hai-Dang Dau (NUS) and I recently released this pre-print, which I'm not half proud of.
arxiv.org/abs/2510.07559
The main problem we solve in it is to construct importance weights for Markov chain Monte Carlo. We achieve it via a method we call harmonization by coupling.
arxiv.org/abs/2510.07559
The main problem we solve in it is to construct importance weights for Markov chain Monte Carlo. We achieve it via a method we call harmonization by coupling.
October 20, 2025 at 4:10 PM
A little self promotion: Hai-Dang Dau (NUS) and I recently released this pre-print, which I'm not half proud of.
arxiv.org/abs/2510.07559
The main problem we solve in it is to construct importance weights for Markov chain Monte Carlo. We achieve it via a method we call harmonization by coupling.
arxiv.org/abs/2510.07559
The main problem we solve in it is to construct importance weights for Markov chain Monte Carlo. We achieve it via a method we call harmonization by coupling.
Applied mechanics
August 11, 2025 at 7:14 PM
Applied mechanics
My little particles are en route for the weekend!
March 28, 2025 at 7:15 PM
My little particles are en route for the weekend!