64 citations · 64 across the 3 of their papers we have counts for
3 papers
stat.CO2025
Adaptive Pseudo-Marginal Algorithm
Sarra Abaoubida, Mylène Bédard, Florian Maire
The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estima…
stat.ME2020
The Linear Lasso: a location model resolution
D. A. S. Fraser, Mylène Bédard
We use location model methodology to guide the least squares analysis of the Lasso problem of variable selection and inference. The nuisance parameter is taken to be an indicator f…
math.PR2007★ 64 cited
Weak convergence of Metropolis algorithms for non-i.i.d. target distributions
Mylène Bédard
In this paper, we shall optimize the efficiency of Metropolis algorithms for multidimensional target distributions with scaling terms possibly depending on the dimension. We propos…