Normal Random Numbers: Using Machine Analysis to Choose the Best Algorithm

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Normal Random Numbers: Using Machine Analysis to Choose the Best Algorithm

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ACM Transactions on Mathematical Software (TOMS) archive
Volume 3 , Issue 4 (December 1977) table of contents

Pages: 346 - 358

Year of Publication: 1977

ISSN:0098-3500

Author

W. H. Payne

Computer Science Department, Washington State University, Pullman, WA

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 3, Downloads (12 Months): 34, Citation Count: 2

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	2	AHRENS, J.H., AND DIETER, U. Extension of Forsythe's method for random sampling from the normal distribution. Math. Comput. 27, 124 (1973), 927-937.
	3	James R. Bell, Algorithm 334: Normal random deviates, Communications of the ACM, v.11 n.7, p.498, July 1968 [doi>10.1145/363397.363547]
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	6	DIETER, U., AND A~RENS, J.H. A combinatorial method for the generation of normally distributed random numbers. Computing 11 (1973), 137-146.
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CITED BY 2

	Amirhossein Alimohammad , Saeed Fouladi Fard , Bruce F. Cockburn , Christian Schlegel, A compact and accurate Gaussian variate generator, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, v.16 n.5, p.517-527, May 2008
	Dong-U Lee , Wayne Luk , John D. Villasenor , Peter Y. K. Cheung, A Gaussian Noise Generator for Hardware-Based Simulations, IEEE Transactions on Computers, v.53 n.12, p.1523-1534, December 2004