Random Number Generation

By Reuven Y. Rubinstein. Encyclopedia of Algorithms, 2008

354 citations recorded by the source corpus.

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. In practice, random variables and other random objects are simulated by deterministic algorithms. The purpose of these algorithms is to produce sequences of numbers or objects whose behavior is very hard to distinguish from that of their “truly random” counterparts, at least for the application of interest. Key requirements may differ depending on the context.For Monte Carlo methods, the main goal is to reproduce the statistical properties on which these methods are based, so that the Monte Carlo estimators behave as expected, whereas for gambling machines and cryptology, observing the sequence of output values for some time should provide no practical advantage for predicting the forthcoming numbers better than by just guessing at random.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/492fec790f9dd10a135f6213091c603e7100fd11

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In Computer Vision and Pattern Recognition

Citations in the corpus

Parallel random numbers: As easy as 1, 2, 3 (2011, neutral): “A PRNG with a period of 2 19937 is not substantially superior to a family of 2 64 PRNGs, each with a period of 2 130 .”
Monte Carlo and Quasi-Monte Carlo Sampling (2009, background): “The material presented here is largely based on [227, 237].”
Fast Random Integer Generation in an Interval (2018, methodology): “There are many efficient techniques to generate high-quality pseudo-random numbers, such as Mersenne Twister [28], Xorshift [27, 32], linear congruential generators [6, 9, 20, 21, 24], and others [22, 26].”
The design of simulation studies in medical statistics (2006, background): “The many different types of random number generator have been detailed elsewhere [11, 12].”
Scalable Bias-Resistant Distributed Randomness (2017, background): “Applications of public randomness are manifold and include the protection of hidden services in the Tor network [34], selection of elliptic curve parameters [2], [40], Byzantine consensus [46], electronic voting [1], random sharding of nodes into groups [35], and non-interactive client-puzzles [37].”