quasi-random

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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.
References in periodicals archive ?
A low-discrepancy sequence is such that its discrepancy decays asymptotically at least as fast as [OMICRON]([(log N).
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.
The van der Corput discovery that the coefficients of the digit expansion of an increasing integer n in base b can be used to define a one-dimensional low-discrepancy sequence inspired Halton [1960] to use s van der Corput sequences with relatively prime bases for different dimensions and to create an s-dimensional low-discrepancy sequence.
An improved low-discrepancy sequence for multi-dimensional Quasi-Monte Carlo integration.
We have investigated the performance of certain common low-discrepancy sequences (i.
Thus, these theoretical results for larger values of s do not give useful descriptions of the properties of the low-discrepancy sequences mentioned.
An alternative approach to the generation of low-discrepancy sequences is to start with points placed into certain equally sized volumes of the unit cube ((t,m,s)-nets).
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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