Descriptive Sampling: An Improvement over Latin Hypercube Sampling

Descriptive Sampling (DS), a Monte Carlo sampling technique based on a deterministic selection of the input values and their random permutation, represents a deep conceptual change on how to carry out a Monte Carlo application. Abandoning the paradigm that a random selection of sample values would be necessary in order to describe random behavior, DS is a rather polemical idea. An interesting issue related to DS are the similarities between it and Latin Hypercube Sampling (LHS) to be discussed in this paper. After a brief description of both methods, it is shown how close DS and LHS are. As such, DS can be seen as a limiting case of LHS and also as an improvement over it. An experiment and a set of empirical results illustrating the relationship between DS and LHS are also presented.

• Type: Conference
• Year: 1997
• Where: WSC
• Authors: Eduardo Saliby
• Keywords: Modeling And Simulation, Monte Carlo, Monte Carlo Application, Monte Carlo Sampling, WSC 1997