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Generation of normal distributions revisited

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  • Takayuki Umeda

    (Nagoya University
    Hokkaido University)

Abstract

Normally distributed random numbers are commonly used in scientific computing in various fields. It is important to generate a set of random numbers as close to a normal distribution as possible for reducing initial fluctuations. Two types of samples from a uniform distribution are examined as source samples for inverse transform sampling methods. Three types of inverse transform sampling methods with new approximations of inverse cumulative distribution functions are also discussed for converting uniformly distributed source samples to normally distributed samples.

Suggested Citation

  • Takayuki Umeda, 2024. "Generation of normal distributions revisited," Computational Statistics, Springer, vol. 39(7), pages 3907-3921, December.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:7:d:10.1007_s00180-024-01468-3
    DOI: 10.1007/s00180-024-01468-3
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    References listed on IDEAS

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    1. Marsaglia, George & Tsang, Wai Wan, 2000. "The Ziggurat Method for Generating Random Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i08).
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