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  2. Mersenne Twister - Wikipedia

    en.wikipedia.org/wiki/Mersenne_Twister

    The 'Extract number' section shows an example where integer 0 has already been output and the index is at integer 1. 'Generate numbers' is run when all integers have been output. For a w -bit word length, the Mersenne Twister generates integers in the range [ 0 , 2 w − 1 ] {\displaystyle [0,2^{w}-1]} .

  3. Random number generation - Wikipedia

    en.wikipedia.org/wiki/Random_number_generation

    Dice are an example of a mechanical hardware random number generator. When a cubical die is rolled, a random number from 1 to 6 is obtained. Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated.

  4. Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Excel

    Microsoft Excel A simple bar graph being created in Excel, running on Windows 11 Developer(s) Microsoft Initial release November 19, 1987 ; 36 years ago (1987-11-19) Stable release 2312 (Build 17126.20132) / January 9, 2024 ; 8 months ago (2024-01-09) Written in C++ (back-end) Operating system Microsoft Windows Type Spreadsheet License Trialware Website microsoft.com microsoft-365 /excel ...

  5. List of random number generators - Wikipedia

    en.wikipedia.org/wiki/List_of_random_number...

    Widely used in many programs, e.g. it is used in Excel 2003 and later versions for the Excel function RAND [8] and it was the default generator in the language Python up to version 2.2. [9] Rule 30: 1983 S. Wolfram [10] Based on cellular automata. Inversive congruential generator (ICG) 1986 J. Eichenauer and J. Lehn [11] Blum Blum Shub: 1986

  6. Monte Carlo method - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_method

    the (pseudo-random) number generator has certain characteristics (e.g. a long "period" before the sequence repeats) the (pseudo-random) number generator produces values that pass tests for randomness; there are enough samples to ensure accurate results; the proper sampling technique is used; the algorithm used is valid for what is being modeled

  7. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    For Monte Carlo simulations, an LCG must use a modulus greater and preferably much greater than the cube of the number of random samples which are required. This means, for example, that a (good) 32-bit LCG can be used to obtain about a thousand random numbers; a 64-bit LCG is good for about 2 21 random samples (a little over two million), etc ...

  8. Randomness test - Wikipedia

    en.wikipedia.org/wiki/Randomness_test

    In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.

  9. Rejection sampling - Wikipedia

    en.wikipedia.org/wiki/Rejection_sampling

    Rejection sampling is based on the observation that to sample a random variable in one dimension, one can perform a uniformly random sampling of the two-dimensional Cartesian graph, and keep the samples in the region under the graph of its density function. [1] [2] [3] Note that this property can be extended to N-dimension functions.

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