Results 161 to 170 of about 8,865,576 (206)

Generation of pseudorandom numbers

Medical Physics, 1986
Computational studies requiring the generation of pseudorandom numbers are becoming increasingly common. These include Monte Carlo methodologies and studies which require the addition of “random” noise to more structured data. Although well‐established random number generators exist, many of these are not suitable for implementation on micro‐ or ...
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An aperiodic pseudorandom number generator [PDF]

Journal of Computational and Applied Mathematics, 1990
A pseudorandom number generator with infinite period is constructed.
Zieliński, Ryszard, Ryszard Zieliński
exaly   +2 more sources

A pseudorandom number generator

SIMULATION, 1985
A simple deterministic algorithm produces a finite sequence which exhibits three iniportant statistical properties. This algo rithm should prove to be useful in simulating some random processes.
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Pseudorandom Number Generators

2017
In this chapter, the author considers existing methods and means of forming pseudo-random sequences of numbers and also are described the main characteristics of random and pseudorandom sequences of numbers. The main theoretical aspects of the construction of pseudo-random number generators are considered.
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Pseudorandom Number Generation by Nonlinear Methods

International Statistical Review / Revue Internationale de Statistique, 1995
A survey paper on some properties of low discrepancy sequences (LDS). The paper summarises results on LDS based on inversive congruential sequences \((y_n)\), \(y_n = \text{inv}(a^*n + b)\), \(\text{inv}(x) = x^{p + 2} \text{mod } p\), \(p\) is a prime greater than 5.
Jürgen Eichenauer-Herrmann, Jurgen Eichenauer-Herrmann   +1 more
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Lower bounds for pseudorandom number generators

30th Annual Symposium on Foundations of Computer Science, 1989
Computational resources necessary to generate pseudorandom strings are studied. In particular, lower bounds are proved for pseudorandom number generators, whereas previous research concentrated on upper bounds. The idea of separation of machine-based complexity classes on the basis of their ability (or inability) to generate pseudorandom strings is ...
Michael Kharitonov, Andrew V. Goldberg, Moti Yung   +2 more
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Teaching labs on pseudorandom number generation

Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education, 2012
This presentation describes our approach to teaching pseudorandom number generation (PRNG) in CS labs. We use PRNG at two universities as an example of an application of sequential circuitry in our digital logic courses. Our goal is for our students to have meaningful assignments, and to relate digital logic not only to the larger CS curriculum, but to
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Dynamic Creation of Pseudorandom Number Generators

2000
We propose a new scheme Dynamic Creation (DC) of pseudorandom number generators (PRNG) for large scale Monte Carlo simulations in parallel or distributed systems. DC receives user’s specification such as word size, period, size of working area, together with a process ID (or a set of IDs).
Matsumoto, M., Nishimura, T.
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Generating Pseudorandom Numbers

1996
Every Monte Carlo experiment relies on the availability of a procedure that supplies sequences of numbers from which arbitrarily selected nonoverlapping subsequences appear to behave like statistically independent sequences and where the variation in an arbitrarily chosen subsequence of length k (≥1) resembles that of a sample drawn from the uniform ...
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Pseudorandom Number Generation

2001
Chapter 8 reveals that every algorithm that generates a sequence of i.i.d. random samples from a probability distribution as output requires a sequence of i.i.d. random samples from u(0, 1) as input. To meet this need, every discrete-event simulation programming language provides a pseudorandom number generator that produces a sequence of nonnegative ...
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