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An improved exact sampling algorithm for the standard normal distribution

Author

Listed:
  • Yusong Du

    (Sun Yat-sen University
    Guangdong Key Laboratory of Information Security Technology)

  • Baoying Fan

    (Sun Yat-sen University)

  • Baodian Wei

    (Sun Yat-sen University
    Guangdong Key Laboratory of Information Security Technology)

Abstract

In 2016, Karney proposed an exact sampling algorithm for the standard normal distribution. In this paper, we study the computational complexity of this algorithm under the random deviate model. Specifically, Karney’s algorithm requires the access to an infinite sequence of independently and uniformly random deviates over the range (0, 1). We give a theoretical estimate of the expected number of uniform deviates used by this algorithm until it completes, and present an improved algorithm with lower uniform deviate consumption. The experimental results also shows that our improved algorithm has better performance than Karney’s algorithm.

Suggested Citation

  • Yusong Du & Baoying Fan & Baodian Wei, 2022. "An improved exact sampling algorithm for the standard normal distribution," Computational Statistics, Springer, vol. 37(2), pages 721-737, April.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:2:d:10.1007_s00180-021-01136-w
    DOI: 10.1007/s00180-021-01136-w
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