Random Sampling using k-vector

Abstract

This work introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the k-vector methodology. The first approach is based on inverse transform sampling using the optimal k-vector to generate the samples by inverting the cumulative distribution. The second approach generates samples by performing random searches in a pre-generated large database previously built by massive inversion of the prescribed nonlinear distribution using the k-vector. Both methods are shown suitable for massive generation of random samples. Examples are provided to clarify these methodologies.

23 pages, 4 figures

References in corpus (1)

• Nonlinear Function Inversion using k-vector

Cited by in corpus (1)

• The n-dimensional k-vector and its application to orthogonal range searching