Probabilistic forecasts in the form of probability distributions over future events have become
popular in several fields including meteorology, hydrology, economics, and demography. In …

The world is becoming increasingly complex, with larger quantities of data available to be
analyzed. It so happens that much of these" big data" that are available are spatio-temporal …

The prediction of crystal structures from first-principles requires highly accurate energies for
large numbers of putative crystal structures. High accuracy of solid state density functional …

Statistical post-processing techniques are now used widely for correcting systematic biases
and errors in the calibration of ensemble forecasts obtained from multiple runs of numerical …

This paper suggests a novel inhomogeneous Markov switching approach for the
probabilistic forecasting of industrial companies' electricity loads, for which the load switches …

In recent years, probabilistic forecasting is an emerging topic, which is why there is a
growing need of suitable methods for the evaluation of multivariate predictions. We analyze …

In spite of the diverse literature on nonstationary spatial modelling and approximate
Gaussian process (GP) methods, there are no general approaches for conducting fully …

Abstract The North American Multi-Model Ensemble (NMME) provides hindcasts and real-
time predictions for monthly mean climate fields at lead times of up to a year. These global …

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow
to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state‐of …

Weather prediction today is performed with numerical weather prediction (NWP) models.
These are deterministic simulation models describing the dynamics of the atmosphere, and …