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Random number generators are chaotic
ACM SIGPLAN Notices, 1989We observe that pseudo-random number generators, familiar to all programmers, are examples of deterministic chaotic dynamical systems. We discuss the implications of this finding and compare computer generation of pseudo-random numbers to the theoretical ideal of a (noncomputable) random sequence.
Charles Herring, Julian I. Palmore
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Random Number Generation [PDF]
, 2011 The fields of probability and statistics are built over the abstract concepts of probability space and random variable. This has given rise to elegant and powerful mathematical theory, but exact implementation of these concepts on conventional computers seems impossible.
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Uniform Random Number Generators
Journal of the ACM, 1965Abstract : This paper discusses the testing of methods for generating uniform numbers in a computer--the commonly used multiplicative and mixed congruential generators as well as two methods. Tests proposed here are more stringent than those usually applied, because the usual tests for randomness have passed several of the commonly used pprocedures ...
M. Donald MacLaren, George Marsaglia
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Computers and Biomedical Research, 1968
Abstract A commonly used uniform random-number generator is examined in light of a genetic-simulation problem. Although this generator is often useful, it proves defective in this case. The author suggests that any proposed generator be checked for the properties needed by the simulation problem at hand.
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Abstract A commonly used uniform random-number generator is examined in light of a genetic-simulation problem. Although this generator is often useful, it proves defective in this case. The author suggests that any proposed generator be checked for the properties needed by the simulation problem at hand.
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Random number generators for microcomputers
Computer Programs in Biomedicine, 1983The feasibility of random number generation using microcomputers is discussed and the appropriateness of alternative algorithms is evaluated on the basis of several criteria of statistical randomness. The relative deficiencies of each algorithm are cited and a modified Fibonacci generator is recommended for use in the microcomputer environment.
W, Rosenbaum, J, Syrotuik, R, Gordon
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Preprints of papers presented at the 14th national meeting of the Association for Computing Machinery on - ACM '59, 1959
One requirement common to all Monte Carlo computer simulations is an abundant and automatic supply of random numbers. For most purposes it generally suffices to draw this supply from the uniform distribution in the unit interval. The mathematical tricks used to convert these samplings to samplings from other distributions are well-known.
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One requirement common to all Monte Carlo computer simulations is an abundant and automatic supply of random numbers. For most purposes it generally suffices to draw this supply from the uniform distribution in the unit interval. The mathematical tricks used to convert these samplings to samplings from other distributions are well-known.
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Quantum random number generator vs. random number generator
2016 International Conference on Communications (COMM), 2016A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic.
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Pseudo Random Number Generators
2013In a first step the definition of randomness and the mathematical definition of random numbers and sequences are addressed. We move on to describe the properties of an ideal random number generator and concentrate then on pseudo random number generators which are the basic tool in the application of stochastic methods in Computational Physics ...
Benjamin A. Stickler, Ewald Schachinger
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Biometric random number generators
Computers & Security, 2004Peer ...
Janusz Szczepanski +4 more
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Gaussian random number generators
ACM Computing Surveys, 2007Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number ...
David B. Thomas +3 more
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