Fast Nonparametric Conditional Density Estimation

Understand

Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx).

• This is important for many tasks, including handling multi-modality and generating prediction intervals.
• Though fundamental and widely applicable, nonparametric conditional density estimators have received relatively little attention from statisticians and little or none from the machine learning community.
• None of that work has been applied to greater than bivariate data, presumably due to the computational difficulty of data-driven bandwidth selection.