2 citations · 2 across the 5 of their papers we have counts for
5 papers
Multiple Jump MCMC: A Scalable Algorithm for Bayesian Inference on Binary Model Spaces
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +2
This article considers Bayesian model inference on binary model spaces. Binary model spaces are used by a large class of models, including graphical models, variable selection, mix…
The Role of Feature Interactions in Graph-based Tabular Deep Learning
Elias Dubbeldam, Reza Mohammadi, Marit Schoonhoven +1
Accurate predictions on tabular data rely on capturing complex, dataset-specific feature interactions. Attention-based methods and graph neural networks, referred to as graph-based…
Modeling Alzheimer's Disease: Bayesian Copula Graphical Model from Demographic, Cognitive, and Neuroimaging Data
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +2
The early detection of Alzheimer's disease (AD) requires an understanding of the relationships between a wide range of features. Conditional independencies and partial correlations…
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +1
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional depe…
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
Reza Mohammadi, Marit Schoonhoven, Lucas Vogels +1
Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability…