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  2. Lehmer random number generator - Wikipedia

    en.wikipedia.org/wiki/Lehmer_random_number_generator

    The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n. The general formula is

  3. Random graph - Wikipedia

    en.wikipedia.org/wiki/Random_graph

    In mathematics, random graph is the general term to refer to probability distributions over graphs. Random graphs may be described simply by a probability distribution, or by a random process which generates them. [1] [2] The theory of random graphs lies at the intersection between graph theory and probability theory.

  4. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    If a ≡ +3, X alternates ±1↔±3, while if a ≡ −3, X alternates ±1↔∓3 (all modulo 8). It can be shown that this form is equivalent to a generator with modulus m/4 and c ≠ 0. [1] A more serious issue with the use of a power-of-two modulus is that the low bits have a shorter period than the high bits.

  5. Inverse transform sampling - Wikipedia

    en.wikipedia.org/wiki/Inverse_transform_sampling

    The idea is illustrated in the following graph: 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 ...

  6. 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.

  7. Fisher–Yates shuffle - Wikipedia

    en.wikipedia.org/wiki/Fisher–Yates_shuffle

    Their description of the algorithm used pencil and paper; a table of random numbers provided the randomness. The basic method given for generating a random permutation of the numbers 1 through N goes as follows: Write down the numbers from 1 through N. Pick a random number k between one and the number of unstruck numbers remaining (inclusive).

  8. George Marsaglia - Wikipedia

    en.wikipedia.org/wiki/George_Marsaglia

    George Marsaglia established the lattice structure of linear congruential generators in the paper "Random numbers fall mainly in the planes", [2] later termed Marsaglia's theorem. [3] This phenomenon means that n -tuples with coordinates obtained from consecutive use of the generator will lie on a small number of equally spaced hyperplanes in n ...

  9. Halton sequence - Wikipedia

    en.wikipedia.org/wiki/Halton_sequence

    As an example, to find the sixth element of the above sequence, we'd write 6 = 1*2 2 + 1*2 1 + 0*2 0 = 110 2, which can be inverted and placed after the decimal point to give 0.011 2 = 0*2-1 + 1*2-2 + 1*2-3 = 3 ⁄ 8. So the sequence above is the same as