Effect of substantially larger training sets on NRE overconfidence

Determine whether increasing the neural ratio estimation training set by an order of magnitude or more substantially reduces the overconfidence of the inferred posteriors.

Background

The authors train neural ratio estimation models on approximately 500,000, 1 million, 1.5 million, and 2 million simulated lens images. Increasing the training set by a factor of four produces no detectable reduction in posterior overconfidence, leaving open whether much larger training sets could improve calibration. Resolving this question would clarify whether the observed overconfidence is primarily a finite-training-sample effect or an intrinsic limitation of the approach and architecture used.