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  2. Fisher–Yates shuffle - Wikipedia

    en.wikipedia.org/wiki/Fisher–Yates_shuffle

    Doing a Fisher–Yates shuffle involves picking uniformly distributed random integers from various ranges. Most random number generators , however — whether true or pseudorandom — will only directly provide numbers in a fixed range from 0 to RAND_MAX, and in some libraries, RAND_MAX may be as low as 32767. [ 18 ]

  3. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    A structure similar to LCGs, but not equivalent, is the multiple-recursive generator: X n = (a 1 X n−1 + a 2 X n−2 + ··· + a k X n−k) mod m for k ≥ 2. With a prime modulus, this can generate periods up to m k −1, so is a useful extension of the LCG structure to larger periods.

  4. Lehmer random number generator - Wikipedia

    en.wikipedia.org/wiki/Lehmer_random_number_generator

    The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n. The general formula is

  5. 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 is generated that cannot be reasonably predicted better than by random chance.

  6. Pseudorandom number generator - Wikipedia

    en.wikipedia.org/wiki/Pseudorandom_number_generator

    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 ...

  7. APL (programming language) - Wikipedia

    en.wikipedia.org/wiki/APL_(programming_language)

    (named deal when dyadic) that returns a vector consisting of a select number (left argument: 6 in this case) of random integers ranging from 1 to a specified maximum (right argument: 40 in this case), which, if said maximum ≥ vector length, is guaranteed to be non-repeating; thus, generate/create 6 random integers ranging from 1 to 40. [73]

  8. Random seed - Wikipedia

    en.wikipedia.org/wiki/Random_seed

    A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.

  9. Residue number system - Wikipedia

    en.wikipedia.org/wiki/Residue_number_system

    A residue numeral system (RNS) is a numeral system representing integers by their values modulo several pairwise coprime integers called the moduli. This representation is allowed by the Chinese remainder theorem, which asserts that, if M is the product of the moduli, there is, in an interval of length M, exactly one integer having any given set of modular values.