Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deterministic Sampling from Uniform Distributions with Sierpinski Space-Filling Curves

Version 1 : Received: 15 November 2020 / Approved: 16 November 2020 / Online: 16 November 2020 (16:51:07 CET)

A peer-reviewed article of this Preprint also exists.

Journal reference: Computational Statistics 2021
DOI: 10.1007/s00180-021-01128-w


In this paper the problem of sampling from uniform probability distributions is approached by means of space-filling curves (SFCs), a topological concept that has found a number of important applications in recent years. Departing from the theoretical fact that they are surjective but not necessarilly injective, the investigation focused upon the structure of the distributions obtained when their domain is swept in a uniform and discrete manner, and the corresponding values used to build histograms, that are approximations of their true PDFs. This work concentrates on the real interval [0,1], and the Sierpinski space-filling curve was chosen because of its favorable computational properties. In order to validate the results, the Kullback-Leibler distance is used when comparing the obtained distributions in several levels of granularity with other already established sampling methods. In truth, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. In this fashion, sequences resulting from this sort of process are not truly random.


Space-filling curves; Ergodic Theory; uniform random number generation.



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