Efficiently accessing the information contained in non-linear and high dimensional
probability distributions remains a core challenge in modern statistics. Traditionally …

Context. Self-calibration methods with the CLEAN algorithm have been widely employed in
very long baseline interferometry (VLBI) data processing in order to correct antenna-based …

Solving Bayesian inference problems approximately with variational approaches can
provide fast and accurate results. Capturing correlation within the approximation requires an …

Information field theory (IFT), the information theory for fields, is a mathematical framework
for signal reconstruction and non-parametric inverse problems. Artificial intelligence (AI) and …

Dynamical system state estimation and parameter calibration problems are ubiquitous
across science and engineering. Bayesian approaches to the problem are the gold standard …

We introduce neural information field filter, a Bayesian state and parameter estimation
method for high-dimensional nonlinear dynamical systems given large measurement …

Modern day Bayesian imaging problems in astrophysics as well as other scientific areas
often result in non-Gaussian and very high-dimensional posterior probability distributions as …

Knowledge on evolving physical fields is of paramount importance in science, technology,
and economics. Dynamical field inference (DFI) addresses the problem of reconstructing a …

Physics is about building a model of the world. Building a model can have two different
interpretations. On the one hand, it can refer to the construction of a model that mimics the …