Random number generators and causality

C. M. GONZALEZ; H. A. LARRONDO; M. T. MARTIN; ANGEL LUIS PLASTINO; O. A. ROSSO

PHYSICS LETTERS A

Elsevier

Lugar: Amsterdam; Año: 2006 vol. 352 p. 421 - 425

0375-9601

  We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG’s) that do not pass Marsaglia’s DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG’s are evaluated and the associated results are compared to those obtained by recourse to Marsaglia’s DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test.that do not pass Marsaglia’s DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG’s are evaluated and the associated results are compared to those obtained by recourse to Marsaglia’s DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test.