Workshop

Bayesian methods for uncertainty quantification

Wednesday 23 October 10.30

Organizer: Rémi Laumont, Technical University of Denmark

Bayesian methods for uncertainty quantification (UQ) are increasingly recognized as a powerful approach to managing uncertainty. At their core, Bayesian methods are about updating our beliefs in light of new evidence. At first we start with a certain level of belief about a situation. As new information becomes available, Bayesian methods refine and adjust this belief. These methods have several advantages. They firstly incorporate prior knowledge or expert opinions.

They also describe the full range of possible outcomes with their associated probabilities. It is crucial for informed decision-making. In a world where data is abundant but often incomplete or uncertain, the capability to integrate prior knowledge and update beliefs with new data is valuable. This topic is particularly relevant in data science where Bayesian UQ stands out as a critical approach for making informed, data-driven decisions. Finally, it has a various and diverse range of application in geoscience or medicine eg.

 

Program


This mini-symposium focuses both on one hand on the recent advances in AI based Bayesian science and on the other hand on practice with real-world examples.

  • Paraskevas Pegios, Phd student in the Department of Applied Mathematics and Computer Science in the Visual Computing section

  • Yi-Shan Wu, Post-Doctoral researcher in the Department of Mathematics and Computer Science at University of South Denmark

  • Siavash Bigdeli, Tenure-Track Associate Professor for deep learning and computer vision in the Visual Computing section at Technical University of Denmark

  • Claus Thorn Ekstrøm, Professor in the Biostatistics Section at University of Copenhagen

  • TBA

Targeted audience

In this mini-symposium, we target a broad audience from the Bayesian theorist to the non-expert scientist.


Organizers
  • Rémi Laumont (main organizer), Post-doctoral fellow in the Department of Applied Mathematics and Computer Science at DTU

  • Jun Yang (co-organizer), Tenure-track Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Copenhagen.