.png){kind=icon}
Return
Gaussian random number generators
.png){kind=icon}
PRE
AI
DOI：10.1145/1287620.1287622.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.
Keywords:
random numbers
Gaussian
normal
simulation
Journal
IF:
28
Papers:
2.5K
Citations:
3.5W
Organization
No organization information available
Cited Papers
A review on the contribution of electron flow in electroactive wetlands: Electricity generation and enhanced wastewater treatment.png)
Chemosphere
ChemosphereIF0
PREAI
ShareSaveShareSaveShareSaveCan Stemness and Chemoresistance Be Therapeutically Targeted via Signaling Pathways in Ovarian Cancer?Cancers
OAAI
CancersIF0
OAAIShareSaveShareSave
ShareSave
View more