2014/08/07 by Ayşe Ferhan Yeşil, Ayse Ferhan Yesil, Yesil, Ayse Ferhan +3
Computer Science · Physics and Astronomy · #Chaos-based Image/Signal Encryption #Computability, Logic, AI Algorithms #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Mathematical Software (cs.MS) #Numerical Methods and Algorithms #Statistical Mechanics (cond-mat.stat-mech) #and Science (cs.CE) #cond-mat.stat-mech #cs.CE #cs.MS
paper · pdf · doi:10.48550/arxiv.1408.1900
9 pages, 4 figures, 1 table
arxiv created 2014/08/07 · openalex publication_date 2014/08/07 · arxiv updated 2014/08/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Emergence of stochastic simulations as an extensively used computational tool for scientific purposes intensified the need for more accurate ways of generating sufficiently long sequences of uncorrelated random numbers. Even though several different methods have been proposed for this end, deterministic algorithms known as pseudo-random number generators (PRNGs) emerged to be the most widely used tool as a replicable, portable and easy to use method to generate such random number sequences. Here, we introduce a simple Poisson process whose simulation gives systematic errors when the very commonly used random number generator of the GNU C Library (Glibc) is utilised. The PRNG of Glibc is an additive lagged Fibonacci generator, the family of such PRNGs are accepted as relatively safe among other PRNGs. The systematic errors indicate complex correlation relations among random numbers which requires a further explanation.