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  2. Module:Random - Wikipedia

    en.wikipedia.org/wiki/Module:Random

    This module is subject to page protection.It is a highly visible module in use by a very large number of pages, or is substituted very frequently. Because vandalism or mistakes would affect many pages, and even trivial editing might cause substantial load on the servers, it is protected from editing.

  3. List of probability distributions - Wikipedia

    en.wikipedia.org/wiki/List_of_probability...

    This does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in the same formalism. The discrete uniform distribution, where all elements of a finite set are equally likely. This is the theoretical distribution model for a balanced coin, an unbiased ...

  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. List (abstract data type) - Wikipedia

    en.wikipedia.org/wiki/List_(abstract_data_type)

    A list may contain the same value more than once, and each occurrence is considered a distinct item. A singly-linked list structure, implementing a list with three integer elements. The term list is also used for several concrete data structures that can be used to implement abstract lists, especially linked lists and arrays.

  6. Sorting algorithm - Wikipedia

    en.wikipedia.org/wiki/Sorting_algorithm

    Shuffling can also be implemented by a sorting algorithm, namely by a random sort: assigning a random number to each element of the list and then sorting based on the random numbers. This is generally not done in practice, however, and there is a well-known simple and efficient algorithm for shuffling: the Fisher–Yates shuffle .

  7. Randomness test - Wikipedia

    en.wikipedia.org/wiki/Randomness_test

    In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.

  8. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    Free Pascal uses a Mersenne Twister as its default pseudo random number generator whereas Delphi uses a LCG. Here is a Delphi compatible example in Free Pascal based on the information in the table above. Given the same RandSeed value it generates the same sequence of random numbers as Delphi.

  9. Reservoir sampling - Wikipedia

    en.wikipedia.org/wiki/Reservoir_sampling

    If we associate with each item of the input a uniformly generated random number, the k items with the largest (or, equivalently, smallest) associated values form a simple random sample. [3] A simple reservoir-sampling thus maintains the k items with the currently largest associated values in a priority queue .