Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...

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 ...

Kernel density estimation is a way to get an idea of where in a region there is a high density of observations, and where there are low density. However, this doesn’t necessarily tell us all that much ...