Non-Parametric Calibration Exploration

Background: Achieving accurate predictive distributions requires models that can adapt their learned shape to the empirical data characteristics.

Question / Future Work: Investigate more flexible, non-parametric temperature parameterizations for probability distribution calibration beyond the specific bi-quadratic function used, particularly given the persistent mismatch observed in the Gaussian Process model’s calibration.