An Improved Exact Sampling Algorithm for the Standard Normal Distribution
Data Structures and Algorithms
2020-08-11 v1
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 an estimate of the expected number of uniform deviates used by this algorithm until outputting a sample value, 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.
Cite
@article{arxiv.2008.03855,
title = {An Improved Exact Sampling Algorithm for the Standard Normal Distribution},
author = {Yusong Du and Baoying Fan and Baodian Wei},
journal= {arXiv preprint arXiv:2008.03855},
year = {2020}
}