385 citations · 530 across the 32 of their papers we have counts for
37 papers
Reparameterization of extreme value framework for improved Bayesian workflow
Théo Moins, Julyan Arbel, Stéphane Girard +1
Using Bayesian methods for extreme value analysis offers an alternative to frequentist ones, with several advantages such as easily dealing with parametric uncertainty or studying…
On the use of a local to improve MCMC convergence diagnostic
Théo Moins, Julyan Arbel, Anne Dutfoy +1
Diagnosing convergence of Markov chain Monte Carlo is crucial and remains an essentially unsolved problem. Among the most popular methods, the potential scale reduction factor, com…
Dependence between Bayesian neural network units
Mariia Vladimirova, Julyan Arbel, Stéphane Girard
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years, with the flagship result that hidden units converge to a Gau…
Bayesian neural network unit priors and generalized Weibull-tail property
Mariia Vladimirova, Julyan Arbel, Stéphane Girard
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years. Hidden units are proven to follow a Gaussian process limit w…
Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions
Mariia Vladimirova, Stephane Girard, Hien Nguyen +1
We propose the notion of sub-Weibull distributions, which are characterised by tails lighter than (or equally light as) the right tail of a Weibull distribution. This novel class g…
Dependence properties and Bayesian inference for asymmetric multivariate copulas
Julyan Arbel, Marta Crispino, Stéphane Girard
We study a broad class of asymmetric copulas introduced by Liebscher (2008) as a combination of multiple - usually symmetric - copulas. The main thrust of the paper is to provide n…