The increasing diversity of scientific and engineering data has driven the development of flexible techniques for inferring probability distributions without assuming a specific parametric family.

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...