(Un)Conditional Sample Generation Based on Distribution Element Trees
arXiv:1711.04632 · doi:10.1080/10618600.2018.1482768
Abstract
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.
published online in the Journal of Computational and Graphical Statistics