Motivated by Bayesian inference with highly informative data we analyze the performance of
random walk-like Metropolis-Hastings algorithms for approximate sampling of increasingly …

We prove that maximum a posteriori estimators are well-defined for diagonal Gaussian
priors $\mu $ on $\ell^ p $ under common assumptions on the potential $\Phi $. Further, we …

This work is a follow-up to our previous contribution (“Convergence of sparse collocation for
functions of countably many Gaussian random variables (with application to elliptic PDEs)” …

Inferring parameter distributions of complex industrial systems from noisy time series data
requires methods to deal with the uncertainty of the underlying data and the used simulation …

Uncertainty Quantification (UQ) is highly requested in computational modeling and
simulation, especially in an industrial context. With the continuous evolution of modern …

In the context of complex applications from engineering sciences the solution of
identification problems still poses a fundamental challenge. In terms of Uncertainty …

The work presented, including the introduction, literature review, bridging texts, discussion,
and conclusion, was authored by myself, Kai Yang, and significantly enhanced under the …

Inverse problems enable integration of observational and experimental data, simulations
and/or mathematical models to make scientific predictions. Solving an inverse problem with …