Gaussian Processes (GPs) are highly expressive, probabilistic models. A ...
Neural Networks play a growing role in many science disciplines, includi...
Efficiently accessing the information contained in non-linear and high
d...
Computer simulations of differential equations require a time discretiza...
CLEAN, the commonly employed imaging algorithm in radio interferometry,
...
Complex systems with many constituents are often approximated in terms o...
A variational Gaussian approximation of the posterior distribution can b...
We address the problem of two-variable causal inference. This task is to...
The inference of deep hierarchical models is problematic due to strong
d...
We present the starblade algorithm, a method to separate superimposed po...
A physical field has an infinite number of degrees of freedom, as it has...
Data from radio interferometers provide a substantial challenge for
stat...
We present a method to reconstruct auto-correlated signals together with...
The inference of correlated signal fields with unknown correlation struc...
In Bayesian statistics probability distributions express beliefs. Howeve...
The calibration of a measurement device is crucial for every scientific
...
Response calibration is the process of inferring how much the measured d...