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The performance of the BBS random-number generator depends on the size of the modulus M and the number of bits per iteration j. While lowering M or increasing j makes the algorithm faster, doing so also reduces the security. A 2005 paper gives concrete, as opposed to asymptotic, security proof of BBS, for a given M and j. The result can also be ...
A typical heavy-ion event generator usually can be less strict in simulating the rare and rather negligible processes found in a hadronic generator, but would need to simulate the following subprocesses, in addition to those in a hadronic generator: Nuclear initial-state; High multiplicity, soft processes; In-medium energy loss
A simple algorithm to generate a permutation of n items uniformly at random without retries, known as the Fisher–Yates shuffle, is to start with any permutation (for example, the identity permutation), and then go through the positions 0 through n − 2 (we use a convention where the first element has index 0, and the last element has index n − 1), and for each position i swap the element ...
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
Studying a random selection is a more practical approach to get a grasp on these questions – and compiling this sample is as simple as hitting Special:Random a bunch of times to record what comes up. Random pages tests by various editors can be found in Category:Random pages tests, although the category is not comprehensive. The concept of ...
A noise generator is a circuit that produces electrical noise (i.e., a random signal). Noise generators are used to test signals for measuring noise figure, frequency response, and other parameters. Noise generators are also used for generating random numbers. [1]
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