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Dynamic random Weyl sampling for drastic reduction of randomness in Monte Carlo integration

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  • Sugita, Hiroshi

Abstract

To reduce randomness drastically in Monte Carlo (MC) integration, we propose a pairwise independent sampling, the dynamic random Weyl sampling (DRWS). DRWS is applicable even if the length of random bits to generate a sample may vary. The algorithm of DRWS is so simple that it works very fast, even though the pseudo-random generator, the source of randomness, might be slow. In particular, we can use a cryptographically secure pseudo-random generator for DRWS to obtain the most reliable numerical integration method for complicated functions.

Suggested Citation

  • Sugita, Hiroshi, 2003. "Dynamic random Weyl sampling for drastic reduction of randomness in Monte Carlo integration," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 529-537.
    • Handle: RePEc:eee:matcom:v:62:y:2003:i:3:p:529-537
    DOI: 10.1016/S0378-4754(02)00231-8
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    Cited by:

    1. Achiha Taku & Sugita Hiroshi & Tonohiro Kenta & Yamamoto Yuto, 2019. "Generation of k-wise independent random variables with small randomness," Monte Carlo Methods and Applications, De Gruyter, vol. 25(3), pages 259-270, September.

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