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WSC
1997

Descriptive Sampling: An Improvement over Latin Hypercube Sampling

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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.
Eduardo Saliby
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where WSC
Authors Eduardo Saliby
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