Neural Systems with Numerically-Matched Input–Output Statistic: Variate Generation

摘要

The aim of this paper is to present a neural system trained to exhibit matched input–output statistic for random samples generation. The learning procedure is based on a cardinal equation from statistics that suggests how to warp an available samples set of known probability density function into a samples set with desired probability distribution. The warping structure is realized by a fully-tunable neural system implemented as a look-up table. Learnability theorems are proven and discussed and the numerical features of the proposed methods are illustrated through computer-based experiments.