1. An improved exact sampling algorithm for the standard normal distribution.
- Author
-
Du, Yusong, Fan, Baoying, and Wei, Baodian
- Subjects
GAUSSIAN distribution ,ALGORITHMS ,COMPUTATIONAL complexity ,RANDOM numbers - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF