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Analysis Of Random Number Generators Using Monte Carlo Simulation

Author

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  • P.D. CODDINGTON

    (Northeast Parallel Architectures Center, Syracuse University 111 College Place, Syracuse, NY 13244, U.S.A.)

Abstract

Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using different generators with each other, or with analytic results. Here we compare the performance of some popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wolff Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.

Suggested Citation

  • P.D. Coddington, 1994. "Analysis Of Random Number Generators Using Monte Carlo Simulation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 547-560.
    • Handle: RePEc:wsi:ijmpcx:v:05:y:1994:i:03:n:s0129183194000726
    DOI: 10.1142/S0129183194000726
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    Cited by:

    1. Yalta, A. Talha & Schreiber, Sven, 2012. "Random Number Generation in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c01).
    2. B. D. McCullough, 2006. "A review of TESTU01," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 677-682, July.

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