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 …

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 …

This paper presents empirical evidence on how judgmental adjustments affect the accuracy
of macroeconomic density forecasts. Judgment is defined as the difference between …

In recent decades, the collection and analysis of survey expectations for economic variables
has gained considerable attention. While some survey sources provide data back to the …

We develop a flexible modeling framework to produce density nowcasts for US inflation at a
trading-day frequency. Our framework (1) combines individual density nowcasts from three …

In many areas of industry and society, including energy, healthcare, and logistics, agents
collect vast amounts of data that are deemed proprietary. These data owners extract …

The purpose of this paper is to show that the effect of the zero-correlation assumption in
combining forecasts can be huge, and that ignoring (positive) correlation can lead to …

Federated learning (FL) is an approach to training machine learning models that takes
advantage of multiple distributed datasets while maintaining data privacy and reducing …

Reconstructing spatially continuous 2-D fields out of their individually derived building
blocks typically introduces artifacts that decrease the overall perceptual quality of the field …

The variance of a linearly combined forecast distribution (or linear pool) consists of two
components: The average variance of the component distributions (average uncertainty') …