
- 1 AHRENS, H.H., AND DIETER, U. Computer methods for sampling from the exponential and normal distributions. Comm. ACM 15, 10 (Oct. 1972), 873-882. Google Scholar
- 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.Google Scholar
- 3 BELL, J.R. Algorithm 334: Normal random deviates. Comm. ACM 11, 7 (July 1968), 498. Google Scholar
- 4 BRENT, R.P. Algorithm 488: A Gaussian pseudo-random number generator. Comm. ACM 17, 12 (Dec. 1974), 704-706. Google Scholar
- 5 DE LuG1s~, B.G A class of algorithms for automatic evaluation of certain elementary functions in a binary computer. Rep. No. 399, Dept. Comptr. Sei., U. of Illinois at Urbana- Champaign, Urbana, Ill., 1970.Google Scholar
- 6 DIETER, U., AND A~RENS, J.H. A combinatorial method for the generation of normally distributed random numbers. Computing 11 (1973), 137-146.Google Scholar
- 7 EARLICH, G. Loopless algorithm for generating permutation, combination, and other combinatorial configurations. J.ACM 20, 3 (July 1973), 500-513. Google Scholar
- 8 FORSYT~IE, G.E. Von Neumann's comparison method for sampling from the normal and other distributions. Math. Comput. 26 (1972), 817-826.Google Scholar
- 9 IvEs, F.M. Permutation enumeration, four new permutation algorithms. Comm. A CM 19, 2 (Feb. 1976), 68-72. Google Scholar
- 10 KNUTH, D.E. The analysis of algorithms. Acres Congres Int. Math. 8 (1970), 269-274.Google Scholar
- 11 KNUTH, D.E. Mathematical analysis of algorithms. Information Processing 71, North- Holland Pub. Co., Amsterdam, 1972, pp. 19-27.Google Scholar
- 12 KNUT~I, D.E. The Art of Computer Programmzng, Vol. 2: Seminumerical Algorithms. Addison-Wesley, Reading, Mass., 1969. Google Scholar
- 13 MARSAOLIA, G., MAcLAI~EN, M.P., AND BRAY, T.A. A fast procedure for generating normal random variables. Comm. ACM 7, 1 (Jan. 1964), 4-10. Google Scholar
- 14 MARSAGLXA, G. Generating a variable from the tail of the normal distribution. Technometrics 6 (1964), 101-102.Google Scholar
- 15 MARSAOLIA, G., AND BRAY, T.A. A convenient method for generating normal variables. SIAM Rev. 6 (1964), 260-264.Google Scholar
- 16 M~GGITT, J.E. Pseudo division and pseudo multiplication processes. IBM J. Res. Develop. 6 (1962), 210-226.Google Scholar
- 17 MULLER, M.E. An inverse method for the generation of random variables on large-scale computers. Math. Tables Aids Comput. 12 (1958), 167-174 (now Math. Comput.).Google Scholar
- 18 Muller, M.E. A comparison of methods for generating normal deviates on digital computers. J. A CM 6 (1959), 376-383. Google Scholar
- 19 ORD-SMITH, R.J. Generation of permutation sequences, P. 2. Comptr. J. 14 (May 1971), 136-139.Google Scholar
- 20 TOCHER, K.D. The Art of Simulation. English Universities Press, London, 1963. Google Scholar
- 21 ZELEN, M., AND SEVEaO, N.C. Probability functions. In Handbook of Mathematical Functions, M. Abramowitz and I.A. Stegun, Eds., Nat. Bur. of Standards, Washington, D.C., 1964.Google Scholar
Index Terms
Normal Random Numbers: Using Machine Analysis to Choose the Best Algorithm
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