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  2. 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 ...

  3. C data types - Wikipedia

    en.wikipedia.org/wiki/C_data_types

    The type int should be the integer type that the target processor is most efficiently working with. This allows great flexibility: for example, all types can be 64-bit. However, several different integer width schemes (data models) are popular.

  4. Multiply-with-carry pseudorandom number generator - Wikipedia

    en.wikipedia.org/wiki/Multiply-with-carry...

    and a carry c r−1 < a. Although the theory of MWC generators permits a > b, a is almost always chosen smaller for convenience of implementation. The state transformation function of an MWC generator is one step of Montgomery reduction modulo p. The state is a large integer with most significant word c n−1 and least significant word x n−r.

  5. List of random number generators - Wikipedia

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

    Prototypical example of a combination generator. Multiply-with-carry (MWC) 1994 G. Marsaglia; C. Koç [22] [23] Complementary-multiply-with-carry (CMWC) 1997 R. Couture and P. L’Ecuyer [24] Mersenne Twister (MT) 1998 M. Matsumoto and T. Nishimura [25] Closely related with LFSRs. In its MT19937 implementation is probably the most commonly used ...

  6. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    If c = 0, the generator is often called a multiplicative congruential generator (MCG), or Lehmer RNG. If c ≠ 0, the method is called a mixed congruential generator. [1]: 4- When c ≠ 0, a mathematician would call the recurrence an affine transformation, not a linear one, but the misnomer is well-established in computer science. [2]: 1

  7. Xorshift - Wikipedia

    en.wikipedia.org/wiki/Xorshift

    A native C implementation of an xorshift+ generator that passes all tests from the BigCrush suite can typically generate a random number in fewer than 10 clock cycles on x86, thanks to instruction pipelining. [12] Rather than using multiplication, it is possible to use addition as a faster non-linear transformation.

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

  9. KISS (algorithm) - Wikipedia

    en.wikipedia.org/wiki/KISS_(algorithm)

    KISS (Keep it Simple Stupid) is a family of pseudorandom number generators introduced by George Marsaglia. [1] [2] [3] Starting from 1998 Marsaglia posted on various newsgroups including sci.math, comp.lang.c, comp.lang.fortran and sci.stat.math several versions of the generators.