7 citations · 15 across the 11 of their papers we have counts for
3 papers · 1 filter
BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme +2
Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable fea…
Scalable Variational Inference for Multinomial Probit Models under Large Choice Sets and Sample Sizes
Gyeongjun Kim, Yeseul Kang, Lucas Kock +2
The multinomial probit (MNP) model is widely used to analyze categorical outcomes due to its ability to capture flexible substitution patterns among alternatives. Conventional like…
Variational inference for hierarchical models with conditional scale and skewness corrections
Lucas Kock, Linda S. L. Tan, Prateek Bansal +1
Gaussian variational approximations are widely used for summarizing posterior distributions in Bayesian models, especially in high-dimensional settings. However, a drawback of such…