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  2. 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.

  3. 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 (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. 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 ...

  5. Permuted congruential generator - Wikipedia

    en.wikipedia.org/wiki/Permuted_Congruential...

    RS: A random (input-dependent) shift, for cases where rotates are more expensive. Again, the output is half the size of the input. Beginning with a 2 b -bit input word, the top b −3 bits are used for a shift amount, which is applied to the next-most-significant 2 b −1 +2 b −3 −1 bits, and the least significant 2 b −1 bits of the ...

  6. Geometric Brownian motion - Wikipedia

    en.wikipedia.org/wiki/Geometric_Brownian_motion

    # Python code for the plot import numpy as np import matplotlib.pyplot as plt mu = 1 n = 50 dt = 0.1 x0 = 100 np. random. seed (1) ...

  7. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    Each row shows the state evolving until it repeats. The top row shows a generator with m = 9, a = 2, c = 0, and a seed of 1, which produces a cycle of length 6. The second row is the same generator with a seed of 3, which produces a cycle of length 2. Using a = 4 and c = 1 (bottom row) gives a cycle length of 9 with any seed in [0, 8].

  8. Applications of randomness - Wikipedia

    en.wikipedia.org/wiki/Applications_of_randomness

    Even if a better random number generator is used, it might be insecure (e.g., the seed might be guessable), producing predictable keys and reducing security to nil. (A vulnerability of this sort was famously discovered in an early release of Netscape Navigator , forcing the authors to quickly find a source of "more random" random numbers.)

  9. Lavarand - Wikipedia

    en.wikipedia.org/wiki/Lavarand

    Lavarand, also known as the Wall of Entropy, is a hardware random number generator designed by Silicon Graphics that worked by taking pictures of the patterns made by the floating material in lava lamps, extracting random data from the pictures, and using the result to seed a pseudorandom number generator.