Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all trades between each counterparty under both market and credit risk. We present a ...

Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn ...

Random fields and Gaussian processes constitute fundamental frameworks in modern probability theory and spatial statistics, providing robust tools for modelling complex dependencies over space and ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

In this talk I will present a theoretical framework that links a set of widely used methods from signal processing to statistical inference procedures. This result will then be used as a conceptual ...