Random Sampling using k-vector. Academic Article uri icon


  • 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.

published proceedings

  • Comput Sci Eng

altmetric score

  • 0.5

author list (cited authors)

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

citation count

  • 5

complete list of authors

  • Arnas, David||Leake, Carl||Mortari, Daniele

publication date

  • January 2019