# From CDF to PDF --- A Density Estimation Method for High Dimensional Data

arXiv: https://arxiv.org/abs/1804.05316 · HTML: https://mlsanity.com/paper/1804.05316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data](https://mlsanity.com/paper/2503.09097/) (2025)

## Abstract (arXiv)

CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results.