What is uncertainty propagation in deep learning evaluation pipelines?

It tracks how uncertainty in inputs and model components affects final predictions.

Uncertainty propagation studies how noise, model uncertainty, and input variability influence downstream predictions. In deep learning pipelines, uncertainty accumulates across preprocessing, feature extraction, and prediction stages. Methods include Monte Carlo sampling, Bayesian networks, and ensemble variance tracking.