quasi-random

(redirected from Low-discrepancy sequence)
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quasi-random

Referring to a method of allocating people to a trial that is not strictly random.

Examples, quasi-random methods
Allocation by date of birth, day of the week, month of the year, by medical record number, or simply allocation of every other person.
Segen's Medical Dictionary. © 2012 Farlex, Inc. All rights reserved.
References in periodicals archive ?
And in this study, our aim is now to present a "low-discrepancy sequence initialized GSA" and to compare its performance with the conventional GSA.
The effect of using a "Sobol" low-discrepancy sequence generator to replace the uniformly distributed pseudorandom number generator previously used to produce the required Gaussian variation is discussed and illustrated by example.
The use of "quasi-Monte Carlo (QMC)" analysis with "low-discrepancy sequences" achieves further computation reduction [8-10].
With the different distributions, essentially the same methodology with respect to statistical delay estimation as used with Gaussian, Pareto, and low-discrepancy sequences in this work can remain valid.
A low-discrepancy sequence is such that its discrepancy decays asymptotically at least as fast as [OMICRON]([(log N).sup.q] /N), where q is the dimension.
Many other constructions of low-discrepancy sequences are known [9, 10, 15, 34], but they are more complicated to generate [19], and they play a significant role only when the number of dimensions is much larger than what we consider here.
Whiten, Computational Investigations of Low-Discrepancy Sequences, ACM Transactions on Mathematical Software, 23(2), 266-294, 1997.
Quasi Monte Carlo integration uses a low-discrepancy sequence to generate sample points and then as in Monte Carlo integration (which uses random sample points) the estimate of the integral is simply the average of the integrand values at the sample point.
An improved low-discrepancy sequence for multi-dimensional Quasi-Monte Carlo integration.
Keywords: data mining, differential ant-stigmergy algorithm, low-discrepancy sequences, meta-heuristic optimization, parameter tuning
The so-called low-discrepancy sequences were specially designed to fulfill all three requirements.
Due to issues related to the speed of convergence, low-discrepancy sequences often can be used in place of either random (or pseudo-random) sampling, since they converge more quickly and can provide more even coverage of a distribution.

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