An improved exact sampling algorithm for the standard normal distribution

Yusong Du (), Baoying Fan and Baodian Wei ()
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Yusong Du: Sun Yat-sen University
Baoying Fan: Sun Yat-sen University
Baodian Wei: Sun Yat-sen University

Computational Statistics, 2022, vol. 37, issue 2, No 8, 737 pages

Abstract: 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.

Keywords: Random number generation; Rejection sampling; Discrete Gaussian distribution (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01136-w

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