Conditional Density Estimation

Evaluation of probabilistic photometric redshift estimation approaches for LSST

Conditional density estimation using Fourier series and neural networks

Most machine learning tools aim at creating good predictions for new samples. However, obtaining 100% is not feasible in most problems, and therefore modeling the uncertainty over such predictions becomes necessary in several applications. This can …

Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation

There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics. In astronomy, for example, one can dramatically improve estimates of the parameters that dictate the evolution of the …

Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting.

In some applications (e.g., in cosmology and economics), the regression E[Z|x] is not adequate to represent the association between a predictor x and a response Z because of multi-modality and asymmetry of f(z|x); using the full density instead of a …