Developments in pseudo-random number generators


Monte Carlo simulations have become a common practice to evaluate a proposed statistical procedure, particularly when it is analytically intractable. Validity of any simulation study relies heavily on the goodness of random variate generators for some specified distributions, which in turn is based on the successful generation of independent variates from the uniform distribution. However, a typical computer-generated pseudo-random number generator (PRNG) is a deterministic algorithm and we know that no PRNG is capable of generating a truly random uniform sequence. Since the foundation of a simulation study is built on the PRNG used, it is extremely important to design a good PRNG. We review some recent developments on PRNGs with nice properties such as high-dimensional equi-distribution, efficiency, long period length, portability, and efficient parallel implementations. WIREs Comput Stat 2017, 9:e1404. doi: 10.1002/wics.1404. For further resources related to this article, please visit the WIREs website.

Publication Title

Wiley Interdisciplinary Reviews: Computational Statistics