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  2. List of random number generators - Wikipedia

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

    List of random number generators. Random number generators are important in many kinds of technical applications, including physics, engineering or mathematical computer studies (e.g., Monte Carlo simulations), cryptography and gambling (on game servers ). This list includes many common types, regardless of quality or applicability to a given ...

  3. Pseudorandom number generator - Wikipedia

    en.wikipedia.org/wiki/Pseudorandom_number_generator

    A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers.

  4. Mersenne Twister - Wikipedia

    en.wikipedia.org/wiki/Mersenne_Twister

    The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto [ ja] (松本 眞) and Takuji Nishimura (西村 拓士). [1] [2] Its name derives from the choice of a Mersenne prime as its period length.

  5. Random number generation - Wikipedia

    en.wikipedia.org/wiki/Random_number_generation

    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. This means that the particular outcome sequence will contain some patterns detectable in hindsight but impossible to foresee.

  6. Pseudorandomness - Wikipedia

    en.wikipedia.org/wiki/Pseudorandomness

    Pseudorandom number generator – Algorithm that generates an approximation of a random number sequence. Low-discrepancy sequence – Type of mathematical sequence. Random number generation – Producing a sequence that cannot be predicted better than by random chance.

  7. Applications of randomness - Wikipedia

    en.wikipedia.org/wiki/Applications_of_randomness

    If one has a pseudo-random number generator whose output is "sufficiently difficult" to predict, one can generate true random numbers to use as the initial value (i.e., the seed), and then use the pseudo-random number generator to produce numbers for use in cryptographic applications.