Calibration of STRmix LRs following the method of Hannig et al.

Calibration may be used to assess whether methods of LR assignment are reliable. Ramos and Gonzalez-Rodriguez [1] introduce the concept of calibration using weather forecasting as an example. Weather forecasters often give a probability of rain. Let us imagine that we wish to check whether these probabilities are being assigned sensibly. If we can assemble a number of days for which the prediction is, for example, around 50%, and of those days about half have precipitation, then this is evidence that this forecaster is operating sensibly - at least in this part of the probability range. This approach for assessing LR calibration is based on assessing the calibration of posterior probabilities for ground-truth known examples with varying prior probabilities. This is based on the LR being the multiplier that converts a prior probability into a posterior probability.