  Recently, distribution element trees (DETs) were introduced as an accurate
and computationally efficient method for density estimation. In this work, we
demonstrate that the DET formulation promotes an easy and inexpensive way to
generate random samples similar to a smooth bootstrap. These samples can be
generated unconditionally, but also, without further complications,
conditionally utilizing available information about certain probability-space
components.
