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2. Lavarand - Wikipedia

   en.wikipedia.org/wiki/Lavarand

   Lavarand, also known as the Wall of Entropy, is a hardware random number generator designed by Silicon Graphics that worked by taking pictures of the patterns made by the floating material in lava lamps, extracting random data from the pictures, and using the result to seed a pseudorandom number generator. [1]

3. List of random number generators - Wikipedia

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

   Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.

4. Random number generation - Wikipedia

   en.wikipedia.org/wiki/Random_number_generation

   Dice are an example of a mechanical hardware random number generator which is 1 through 6. 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.

5. Drawing lots (decision making) - Wikipedia

   en.wikipedia.org/wiki/Drawing_lots_(decision_making)

   Drawing lots or drawing straws is a selection method, or a form of sortition, that is used by a group to choose one member of the group to perform a task after none has volunteered for it. The same practice can be used also to choose one of several volunteers, should an agreement not be reached.

6. Mersenne Twister - Wikipedia

   en.wikipedia.org/wiki/Mersenne_Twister

   The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto (松本 眞) and Takuji Nishimura (西村 拓士). [1] [2] Its name derives from the choice of a Mersenne prime as its period length. The Mersenne Twister was designed specifically to rectify most of the flaws found in older PRNGs.

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.