2 papers
cs.LG2026
MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems
Heikki Haario, Zhi-Song Liu, Martin Simon +1
Neural networks are a commonly used approach to replace physical models with computationally cheap surrogates. Parametric uncertainty quantification can be included in training, as…
q-fin.PM2024
Uncertainty Quantification in Portfolio Temperature Alignment
Hendrik Weichel, Aleksandr Zinovev, Heikki Haario +1
We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X-Degree Compatibility (XDC) approach with the scientifi…