Generation of normal distributions revisited

Takayuki Umeda ()
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Takayuki Umeda: Nagoya University

Computational Statistics, 2024, vol. 39, issue 7, No 19, 3907-3921

Abstract: 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.

Keywords: Random number generation; Normal distribution; Inversion transform sampling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-024-01468-3

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