Results 231 to 240 of about 193,973 (281)
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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 ...
MacLaren, M. D., Marsaglia, G.
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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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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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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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2021
To perform a Monte Carlo approximation, we have to generate random variables (rv.) on a computer according to a given df. F. In this chapter, we will discuss some commonly used procedures and their application under R. Since most of the widely used distributions are implemented in R, random variables according to these distributions can easily be ...
Gerhard Dikta, Marsel Scheer
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To perform a Monte Carlo approximation, we have to generate random variables (rv.) on a computer according to a given df. F. In this chapter, we will discuss some commonly used procedures and their application under R. Since most of the widely used distributions are implemented in R, random variables according to these distributions can easily be ...
Gerhard Dikta, Marsel Scheer
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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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2023
The purpose of the chapter is to introduce basic concepts of random sequence generation that can be used in the simulation modeling of random flows. In the first part, features of random number generators (RNGs) are presented, distinguishing the two directions of realization – true RNGs and pseudo-RNGs.
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The purpose of the chapter is to introduce basic concepts of random sequence generation that can be used in the simulation modeling of random flows. In the first part, features of random number generators (RNGs) are presented, distinguishing the two directions of realization – true RNGs and pseudo-RNGs.
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International Journal of Modern Physics C, 1991
In large-scale Monte Carlo simulations, reliable random numbers will soon be needed at bit rates of 1 GHz or more. Therefore, existing recipes for the generation of random numbers have to be improved. This is not easy, due to the many unrelated and laborious statistical tests needed to compensate for the lack of an accepted and operational definition ...
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In large-scale Monte Carlo simulations, reliable random numbers will soon be needed at bit rates of 1 GHz or more. Therefore, existing recipes for the generation of random numbers have to be improved. This is not easy, due to the many unrelated and laborious statistical tests needed to compensate for the lack of an accepted and operational definition ...
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2015
The need to generate random numbers arises very often. Most games programs, programs that simulate the real world, almost always need the ability to generate random numbers. Testing a complex program usually needs random input at some point to validate that the program works under diverse conditions, and it’s often convenient to generate such input ...
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The need to generate random numbers arises very often. Most games programs, programs that simulate the real world, almost always need the ability to generate random numbers. Testing a complex program usually needs random input at some point to validate that the program works under diverse conditions, and it’s often convenient to generate such input ...
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2010
Much of this book deals with simulation methods for probability models, also called Monte Carlo methods. We have seen a few introductory examples in Chapter 1. Even for some models that are easy to specify in a theoretical form, it may be difficult or impossible to “do the math” necessary to obtain the numerical results required in practice. Because of
P.J. Pashley, A. Amodeo
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Much of this book deals with simulation methods for probability models, also called Monte Carlo methods. We have seen a few introductory examples in Chapter 1. Even for some models that are easy to specify in a theoretical form, it may be difficult or impossible to “do the math” necessary to obtain the numerical results required in practice. Because of
P.J. Pashley, A. Amodeo
+4 more sources