Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …

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

We document the impact of COVID-19 on inflation modelling within a vector autoregression
(VAR) model and provide guidance for forecasting euro area inflation during the pandemic …

Forecasting plays an indispensable role in grid integration of solar energy, which is an
important pathway toward the grand goal of achieving planetary carbon neutrality. This …

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 is concerned with the formulation of an adequate likelihood function in the
application of Bayesian epistemology to uncertainty quantification of hydrologic models. We …

Adaptive importance sampling is a class of techniques for finding good proposal
distributions for importance sampling. Often the proposal distributions are standard …

In a world where climate change, population growth, and global diseases threaten economic
access to food, policies and contingency plans can strongly benefit from reliable forecasts of …

Quantile regression methods are increasingly used to forecast tail risks and uncertainties in
macroeconomic outcomes. This paper reconsiders how to construct predictive densities from …