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Xorshift RNGs

Author

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  • Marsaglia, George

Abstract

Description of a class of simple, extremely fast random number generators (RNGs) with periods 2k - 1 for k = 32, 64, 96, 128, 160,'2. These RNGs seem to pass tests of randomness very well.

Suggested Citation

  • Marsaglia, George, 2003. "Xorshift RNGs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i14).
    • Handle: RePEc:jss:jstsof:v:008:i14
    DOI: http://hdl.handle.net/10.18637/jss.v008.i14
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    Cited by:

    1. Fernández, J.L. & Ferreiro, A.M. & García-Rodríguez, J.A. & Leitao, A. & López-Salas, J.G. & Vázquez, C., 2013. "Static and dynamic SABR stochastic volatility models: Calibration and option pricing using GPUs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 55-75.
    2. Kato, Kensuke, 2016. "Long-range Ising model for credit portfolios with heterogeneous credit exposures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1103-1119.
    3. Marsaglia, George & Tsang, Wai Wan & Wang, Jingbo, 2004. "Fast Generation of Discrete Random Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i03).
    4. Leong, Philip H. W. & Zhang, Ganglie & Lee, Dong-U & Luk, Wayne & Villasenor, John, 2005. "A Comment on the Implementation of the Ziggurat Method," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i07).
    5. Löhndorf, Nils, 2016. "An empirical analysis of scenario generation methods for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 255(1), pages 121-132.

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