The areal modeling of the extremes of a natural process such as rainfall or temperature is
important in environmental statistics; for example, understanding extreme areal rainfall is …

The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …

We advocate an optimization-centric view of Bayesian inference. Our inspiration is the
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …

We propose a framework for general Bayesian inference. We argue that a valid update of a
prior belief distribution to a posterior can be made for parameters which are connected to …

Recent studies have revealed evidence of trends in the median or mean flood discharge in
Europe over the last 5 decades, with clear and coherent regional patterns. The aim of this …

Max-stable processes are the natural analogues of the generalized extreme value
distribution when modelling extreme events in space and time. Under suitable conditions …

We advocate an optimization-centric view on and introduce a novel generalization of
Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite …

The global and regional influence of the El Niño-Southern Oscillation (ENSO) phenomenon
on extreme precipitation was analyzed using a global database comprising over 7000 high …

Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a
small labeled dataset, is an important step on the path toward human-like machine learning …

Recent studies have shown evidence of increasing and decreasing trends in mean annual
floods and flood quantiles across Europe. Studies attributing observed changes in flood …