Students in an Introduction to Lifespan Development course ( N = 108) participated in an experiment with a 2x2 randomized factorial design that explored the effects of document generation (control, document generation; DGT) and presentation format (single text, multiple documents) on historical ...
This paper describes and analyses some methods of generating pseudorandom sequences suitable for use in the simulation of white Gaussian noise. The criteria are somewhat different than those customary in pseudorandom number generation in other applications such as Monte Carlo methods. The methods ...
Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom...
不重复的随机数的产生(Thegenerationofrandomnumbersthat arenotrepeated) Howdoyougeneraterandomnumbersthatdonotrepeat? 2010-07-23 Theusualwaytogeneraterandomnumbersisnottothinkabout repetition,becauseevenrepetitionisanormalthingin probability.Butwhataboutrandomdatathatdoesnotneedto berepeatedinsomecases? Ithink...
The quality of generated random numbers is verified with the standard statistical test batteries Diehard and TestU01. We also present two real-world application studies with multiple RNG streams: the Ziggurat method for generating normal random variables and a Monte Carlo photon-transport simulation....
To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown ...
This simulation study based on 8685000 random numbers and 27000 tests of significance shows that ability to simulate random data from Bernoulli distribution is best in SAS and is closely followed by R Language, while Minitab showed the worst performance among compared packages./p...
It is very easy to implement, with the main hurdle being the generation of random numbers. Monte Carlo methods are inherently parallelizable. This last point is a major advantage, allowing Monte Carlo solutions to easily scale to multiple nodes in a networked cluster, or to multiple ...
Random number generation is a key element of stochastic simulations. It has been widely studied for sequential applications purposes, enabling us to reliably use pseudo-random numbers in this case. Unfortunately, we cannot be so enthusiastic when dealing with parallel stochastic simulations. Many applic...
index of the behavior of n -tuples of consecutively generated numbers. In any Monte Carlo or simulation problem where n supposedly independent random numbers are required at each step, this behavior is likely to be important. Finally, since the tests presented here differ in certain details from...