2 citations · 5 across the 7 of their papers we have counts for
8 papers
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…
Multilevel Monte Carlo learning
Thomas Gerstner, Bastian Harrach, Daniel Roth +1
In this work, we study the approximation of expected values of functional quantities on the solution of a stochastic differential equation (SDE), where we replace the Monte Carlo e…
Brexit Risk Implied by the SABR Martingale Defect in the EUR-GBP Smile
Petteri Piiroinen, Lassi Roininen, Martin Simon
We construct a data-driven statistical indicator for quantifying the tail risk perceived by the EURGBP option market surrounding Brexit-related events. We show that under lognormal…
Asset Price Bubbles: An Option-based Indicator
Petteri Piiroinen, Lassi Roininen, Tobias Schoden +1
We construct a statistical indicator for the detection of short-term asset price bubbles based on the information content of bid and ask market quotes for plain vanilla put and cal…
Probabilistic Interpretation of the Calderón Problem
Petteri Piiroinen, Martin Simon
In this paper, we use the theory of symmetric Dirichlet forms to give a probabilistic interpretation of Calderón's inverse conductivity problem in terms of reflecting diffusion pro…
From Feynman-Kac Formulae to Numerical Stochastic Homogenization in Electrical Impedance Tomography
Petteri Piiroinen, Martin Simon
In this paper, we use the theory of symmetric Dirichlet forms to derive Feynman-Kac formulae for the forward problem of electrical impedance tomography with possibly anisotropic, m…