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 ...

Modal regression focuses on estimating the conditional mode—the most probable outcome—given a set of predictors, thereby offering insights that complement classical mean and quantile regression. By ...