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Behavior Research Methods
Article . 1981 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Effective random seeding of random number generators

Authors: Howard L. Kaplan;

Effective random seeding of random number generators

Abstract

To obtain different sequences of values from a random number generating algorithm, we must start with different seed values or discard a variable number of initial samples. For maximum independence among sequences, differences must include the lowest order seed bits, and some generators require one to wait many samples for differences to propagate to the significant bits. While it is straightforward to generate two statistically independent sequences, it is surprisingly difficult to generate three or more such sequences.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
56
Average
Top 10%
Average
bronze