Towards Calibrated Probabilistic Forecasts for Events of Interest via Outcome-Conditional Recalibration

Calibration is an essential requirement for probabilistic predictions to be useful for decision making. While state-of-the-art prediction methods often yield miscalibrated predictive distributions, several post-hoc recalibration schemes have been proposed to generate calibrated predictions. However,