Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules

arXiv:2402.10727v1 Announce Type: cross
Abstract: Distinguishing sources of predictive uncertainty is of crucial importance in the application of forecasting models across various domains. Despite the presence of a great variety of proposed uncertainty measures, there are no strict definitions to disentangle them. Furthermore, the relationship between different measures of uncertainty quantification remains somewhat unclear. In this work, we introduce a general framework, rooted in statistical reasoning, which not only allows the creation of new uncertainty measures but also clarifies their …

abstract application arxiv cs.lg definitions domains forecasting importance predictive quantification relationship risk rules scoring stat.ml them type uncertainty via