Quantifying systemic risk using Bayesian networks

Quantifying systemic risk using Bayesian networks

Sumit Sourabh, Markus Hofer and Drona Kandhai develop a novel framework using Bayesian networks to capture distress dependence in the context of counterparty credit risk. Then, they apply this methodology to a wrong-way risk model and stress-scenario testing. Their...

PhD candidate on Neural Networks as Dynamical Systems

This Ph.D. position is part of a cross-theme project within the Institute of Informatics. We seek a multidisciplinary researcher who can study deep neural networks in the context of complex adaptive systems analysis. Specifically, the aim is to reformulate and...