Improving randomness characterization through Bayesian model selection.

ABSTRACT: Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing whether a source produce truly random sequences are still missing. Current methods either do not rely on a formal description of randomness (NIST test suite) on the one hand, or are inapplicable in principle (the characterization derived from the Algorithmic Theory of Information), on the other, for they require testing all the possible computer programs that could produce the sequence to be analysed. Here we present a rigorous method that overcomes these problems based on Bayesian model selection. We d

SUBMITTER: Diaz Hernandez Rojas R 

PROVIDER: S-EPMC5465194 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature