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  2. 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.

  3. List of random number generators - Wikipedia

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

    These approaches combine a pseudo-random number generator (often in the form of a block or stream cipher) with an external source of randomness (e.g., mouse movements, delay between keyboard presses etc.). /dev/random – Unix-like systems; CryptGenRandom – Microsoft Windows; Fortuna

  4. Blum Blum Shub - Wikipedia

    en.wikipedia.org/wiki/Blum_Blum_Shub

    Blum Blum Shub takes the form + =, where M = pq is the product of two large primes p and q.At each step of the algorithm, some output is derived from x n+1; the output is commonly either the bit parity of x n+1 or one or more of the least significant bits of x n+1.

  5. Gaussian process - Wikipedia

    en.wikipedia.org/wiki/Gaussian_process

    Gaussian processes are also commonly used to tackle numerical analysis problems such as numerical integration, solving differential equations, or optimisation in the field of probabilistic numerics. Gaussian processes can also be used in the context of mixture of experts models, for example.

  6. 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 ...

  7. Stochastic optimization - Wikipedia

    en.wikipedia.org/wiki/Stochastic_optimization

    Fred W. Glover [22] argues that reliance on random elements may prevent the development of more intelligent and better deterministic components. The way in which results of stochastic optimization algorithms are usually presented (e.g., presenting only the average, or even the best, out of N runs without any mention of the spread), may also ...

  8. Inverse transform sampling - Wikipedia

    en.wikipedia.org/wiki/Inverse_transform_sampling

    Inverse transformation sampling takes uniform samples of a number between 0 and 1, interpreted as a probability, and then returns the smallest number such that () for the cumulative distribution function of a random variable. For example, imagine that is the standard normal distribution with mean zero and standard deviation one. The table below ...

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