Workshop BAYST: BAYesian latent variable modelling for STructural learning

1.-3. March 2027, NTI Lecture Hall, KIT Campus South

Abstract
The BAYST workshop aims to bridge recent advances in Bayesian structural learning with applied sciences, fostering international collaboration between theoretical and applied researchers. Moreover, it will serve as the kick-off meeting for the new proposed section on Bayesian Structural Learning from the International Society of Bayesian Analysis (ISBA) which aims to provide an inclusive home for researchers working with high-dimensional or structurally complex data, whether methodologically or applied.

The BAYST workshop will bring together junior and senior researchers from statistics, applied mathematics, and application-specific fields to exchange knowledge on recent methodological innovations and emerging applications of Bayesian structure learning.

The scientific focus spans: 

  1. Dependence analysis and factor modelling. 
  2. Clustering.
  3. Model and variable selection.
  4. Causal discovery and inference.
  5. Flexible regression and spatio-temporal models.

The workshop will feature 20 invited talks over 2 and half days. In addition to the invited talks, other participants will have the opportunity to present posters.