Random Sampling Using $k$k-Vector Academic Article uri icon

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.

author list (cited authors)

  • Arnas, D., Leake, C., & Mortari, D.

publication date

  • January 1, 2019 11:11 AM