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  2. 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.

  3. 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 ...

  4. Ziggurat algorithm - Wikipedia

    en.wikipedia.org/wiki/Ziggurat_algorithm

    Ignoring for a moment the problem of layer 0, and given uniform random variables U 0 and U 1 ∈ [0,1), the ziggurat algorithm can be described as: Choose a random layer 0 ≤ i < n. Let x = U 0 x i. If x < x i +1, return x. Let y = y i + U 1 (y i +1 − y i). Compute f(x). If y < f(x), return x. Otherwise, choose new random numbers and go back ...

  5. Continuous uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Continuous_uniform...

    On the other hand, the uniformly distributed numbers are often used as the basis for non-uniform random variate generation. If u {\displaystyle u} is a value sampled from the standard uniform distribution, then the value a + ( b − a ) u {\displaystyle a+(b-a)u} follows the uniform distribution parameterized by a {\displaystyle a} and b ...

  6. Inverse transform sampling - Wikipedia

    en.wikipedia.org/wiki/Inverse_transform_sampling

    Random numbers y i are generated from a uniform distribution between 0 and 1, i.e. Y ~ U(0, 1). They are sketched as colored points on the y-axis. Each of the points is mapped according to x=F −1 (y), which is shown with gray arrows for two example points. In this example, we have used an exponential distribution.

  7. Box–Muller transform - Wikipedia

    en.wikipedia.org/wiki/Box–Muller_transform

    When a computer is used to produce a uniform random variable it will inevitably have some inaccuracies because there is a lower bound on how close numbers can be to 0. If the generator uses 32 bits per output value, the smallest non-zero number that can be generated is 2 − 32 {\displaystyle 2^{-32}} .

  8. Sobol sequence - Wikipedia

    en.wikipedia.org/wiki/Sobol_sequence

    As for the generation of Sobol’ numbers, they are clearly aided by the use of Gray code () = ⌊ / ⌋ instead of n for constructing the n-th point draw. Suppose we have already generated all the Sobol’ sequence draws up to n − 1 and kept in memory the values x n −1, j for all the required dimensions.

  9. Marsaglia polar method - Wikipedia

    en.wikipedia.org/wiki/Marsaglia_polar_method

    The Marsaglia polar method [1] is a pseudo-random number sampling method for generating a pair of independent standard normal random variables. [2]Standard normal random variables are frequently used in computer science, computational statistics, and in particular, in applications of the Monte Carlo method.