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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. 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.

  4. Cryptographically secure pseudorandom number generator

    en.wikipedia.org/wiki/Cryptographically_secure...

    In the asymptotic setting, a family of deterministic polynomial time computable functions : {,} {,} for some polynomial p, is a pseudorandom number generator (PRNG, or PRG in some references), if it stretches the length of its input (() > for any k), and if its output is computationally indistinguishable from true randomness, i.e. for any probabilistic polynomial time algorithm A, which ...

  5. 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

  6. Universal hashing - Wikipedia

    en.wikipedia.org/wiki/Universal_hashing

    Since is a random odd integer and odd integers have inverses in the ring, it follows that () will be uniformly distributed among -bit integers with the least significant set bit on position . The probability that these bits are all 0's or all 1's is therefore at most 2 / 2 M = 2 / m {\displaystyle 2/2^{M}=2/m} .

  7. Diehard tests - Wikipedia

    en.wikipedia.org/wiki/Diehard_tests

    Analyze sequences of five consecutive random numbers. The 120 possible orderings should occur with statistically equal probability. This is the OPERM5 test. It looks at a sequence of one million 32-bit random integers. Each set of five consecutive integers can be in one of 120 states, for the 5! possible orderings of five numbers.

  8. Rolling hash - Wikipedia

    en.wikipedia.org/wiki/Rolling_hash

    A rolling hash (also known as recursive hashing or rolling checksum) is a hash function where the input is hashed in a window that moves through the input.. A few hash functions allow a rolling hash to be computed very quickly—the new hash value is rapidly calculated given only the old hash value, the old value removed from the window, and the new value added to the window—similar to the ...

  9. Random number - Wikipedia

    en.wikipedia.org/wiki/Random_number

    Random numbers are frequently used in algorithms such as Knuth's 1964-developed algorithm [1] for shuffling lists. (popularly known as the Knuth shuffle or the Fisher–Yates shuffle, based on work they did in 1938). In 1999, a new feature was added to the Pentium III: a hardware-based random number generator.