16 citations · 17 across the 3 of their papers we have counts for
4 papers
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines +1
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because th…
f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models
Belhal Karimi, Marc Lavielle, Eric Moulines
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely us…
Efficient Metropolis-Hastings Sampling for Nonlinear Mixed Effects Models
Belhal Karimi, Marc Lavielle
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely us…
Random threshold for linear model selection, revisited
Merlin Keller, Marc Lavielle
In [Lavielle and Ludena 07], a random thresholding metho d is intro duced to select the significant, or non null, mean terms among a collection of independent random variables, and…