45 citations · 151 across the 18 of their papers we have counts for
4 papers · 1 filter
Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1
Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Parametric MMD Estimation with Missing Values: Robustness to Missingness and Data Model Misspecification
Badr-Eddine Chérief-Abdellatif, Jeffrey Näf
In the missing data literature, the Maximum Likelihood Estimator (MLE) is celebrated for its ignorability property under missing at random (MAR) data. However, its sensitivity to m…
On the Asymptotics of Importance Weighted Variational Inference
Badr-Eddine Cherief-Abdellatif, Randal Douc, Arnaud Doucet +1
For complex latent variable models, the likelihood function is not available in closed form. In this context, a popular method to perform parameter estimation is Importance Weighte…