Frontiers in Dependence Modeling

Abstract: 

Dependence modeling has evolved into a cornerstone of modern statistics. It provides the necessary tools to move beyond simple linear correlations and Gaussian assumptions toward a comprehensive understanding of complex, possibly non-linear multivariate relationships. Hosted under the MathSEE’s initiative to foster interdisciplinary discussion, this mini-symposium highlights recent developments in dependence modeling in both theoretical research and a diverse range of applications led by young researchers. Central to our discussion is the role of inference. On one hand, Bayesian inference offers a robust framework for uncertainty quantification and allows practitioners to integrate expert prior knowledge into complex model architectures. On the other hand, likelihood-based inference provides a computationally efficient framework that ensures scalability and statistical consistency. Ultimately, this mini-symposium serves as a collaborative forum to bridge the gap between theoretical and applied scientists, with a particular focus on statistical methods addressing challenges in science and society.

Confirmed Speakers:

Luciana Dalla Valle (University of Turin)
Christopher Bülte (Ludwig Maximilian University of Munich)
Ferdinand Buchner (Technical University of Munich)
Matthias Herp (Georg August University of Göttingen)