Predictive Distribution

Predictive distribution research focuses on generating probabilistic forecasts, moving beyond single-point predictions to quantify uncertainty and improve decision-making under uncertainty. Current research emphasizes developing flexible and robust methods for estimating these distributions, employing diverse model architectures such as Gaussian processes, Bayesian neural networks, conditional diffusion models, and gradient-boosted trees, often incorporating techniques like model averaging and recalibration to enhance accuracy and calibration. This work is significant because well-calibrated predictive distributions are crucial for reliable risk assessment, improved model selection, and more informed decision-making across various scientific and engineering applications.