I have a dataset that contains measurements for a physical process(continuous set of values). However, these values are not measured in reality but are rather best estimates based on interpretations. So they come with a high side & low side for each measurement /interpretation. I am trying to do a probabilistic calibration of a parametric model that predicts the data. The goal here is to learn the parameters of this model (joint distribution) that accounts for the uncertainty in the calibration data.