research
∙
06/21/2022
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)
Gaussian Processes (GPs) are highly expressive, probabilistic models. A ...
share
research
∙
10/28/2021
Probabilistic Autoencoder using Fisher Information
Neural Networks play a growing role in many science disciplines, includi...
research
∙
05/21/2021
Geometric variational inference
Efficiently accessing the information contained in non-linear and high d...
research
∙
10/13/2020
Probabilistic simulation of partial differential equations
Computer simulations of differential equations require a time discretiza...
research
∙
08/26/2020
Comparison of classical and Bayesian imaging in radio interferometry
CLEAN, the commonly employed imaging algorithm in radio interferometry, ...
research
∙
02/05/2019
Field dynamics inference for local and causal interactions
Complex systems with many constituents are often approximated in terms o...
research
∙
01/30/2019
Metric Gaussian Variational Inference
A variational Gaussian approximation of the posterior distribution can b...
research
∙
12/24/2018
Bayesian Causal Inference
We address the problem of two-variable causal inference. This task is to...
research
∙
12/11/2018
Encoding prior knowledge in the structure of the likelihood
The inference of deep hierarchical models is problematic due to strong d...
research
∙
04/16/2018
Separating diffuse from point-like sources - a Bayesian approach
We present the starblade algorithm, a method to separate superimposed po...
research
∙
04/10/2018
Information theory for fields
A physical field has an infinite number of degrees of freedom, as it has...
research
∙
03/06/2018
Radio Imaging With Information Field Theory
Data from radio interferometers provide a substantial challenge for stat...
research
∙
11/08/2017
Inference of signals with unknown correlation structure from non-linear measurements
We present a method to reconstruct auto-correlated signals together with...
research
∙
12/26/2016
Correlated signal inference by free energy exploration
The inference of correlated signal fields with unknown correlation struc...
research
∙
10/27/2016
Optimal Belief Approximation
In Bayesian statistics probability distributions express beliefs. Howeve...
research
∙
10/23/2014
Signal inference with unknown response: Calibration-uncertainty renormalized estimator
The calibration of a measurement device is crucial for every scientific ...
research
∙
12/04/2013
