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- Title
An improved exact sampling algorithm for the standard normal distribution.
- Authors
Du, Yusong; Fan, Baoying; Wei, Baodian
- 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.
- Subjects
GAUSSIAN distribution; ALGORITHMS; COMPUTATIONAL complexity; RANDOM numbers
- Publication
Computational Statistics, 2022, Vol 37, Issue 2, p721
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-021-01136-w