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  2. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    Using a = 4 and c = 1 (bottom row) gives a cycle length of 9 with any seed in [0, 8]. A linear congruential generator ( LCG) is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a discontinuous piecewise linear equation. The method represents one of the oldest and best-known pseudorandom number generator algorithms.

  3. Mersenne Twister - Wikipedia

    en.wikipedia.org/wiki/Mersenne_Twister

    Mersenne Twister. The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto (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.

  4. Euler–Maruyama method - Wikipedia

    en.wikipedia.org/wiki/Euler–Maruyama_method

    The following Python code implements the Euler–Maruyama method and uses it to solve the ... The random numbers for ... (TS. size) ys [0] = Y_INIT for i in range (1 ...

  5. Applications of randomness - Wikipedia

    en.wikipedia.org/wiki/Applications_of_randomness

    Randomness has many uses in science, art, statistics, cryptography, gaming, gambling, and other fields. For example, random assignment in randomized controlled trials helps scientists to test hypotheses, and random numbers or pseudorandom numbers help video games such as video poker . These uses have different levels of requirements, which ...

  6. 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 ), [1] is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value ...

  7. 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.

  8. Random seed - Wikipedia

    en.wikipedia.org/wiki/Random_seed

    Random seed. A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator . For a seed to be used in a pseudorandom number generator, it does not need to be random. Because of the nature of number generating algorithms, so long as the original seed is ignored, the rest of the values that ...

  9. Bogosort - Wikipedia

    en.wikipedia.org/wiki/Bogosort

    import random # bogosort # what happens is there is a random array that is generated by the last function # the first function checks whether the array is sorted or not # the second function repeatedly shuffles the array for as long as it remains unsorted # and that's it # happy coding => # this function checks whether or not the array is sorted def is_sorted (random_array): for i in range (1 ...