Number - Pseudo-random Numbers

> (Data|State) Management and Processing > (Data Type|Data Structure) > Number, Numeric, Quantity

1 - About

Pseudo-random numbers is a sequence of numbers that is predictable if you know the seed. Because true randomness is unpredictable, this is called pseudo randomness (If you know the seed, you can predict the output)

A pseudo-random sequence has the following properties:

  • The sequence should never repeat itself
  • The numbers should be spread evenly across the numeric domain (for instance between 0 and 10)
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3 - Example of bad random sequence

Type Sequence Example
Uniform sequence 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Repeated sequence 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
Too many low numbers 1 3 2 5 3 9 1 2 4 2 5 1 1 2 8 1 5 2 3 4
Too many even numbers 2 8 4 6 0 9 8 2 4 8 6 4 2 2 5 1 4 8 6 2

4 - Generator

This sequence is generated through a random generator that requires a seed value. For the same seed value, you will get the same sequence of Pseudo-random Number.

Random number generators are pseudo-random number generators because the output of a deterministic program cannot really be random. They are complex because they are deterministic programs that must give the illusion of being non-deterministic.

A random generator may be considered high-quality for simulation while being considered unacceptable for cryptography.

4.1 - With Distribution

A frequent problem in statistical simulations (the Monte Carlo method) is the generation of pseudo-random numbers that are distributed in a given way. Most algorithms are based on a pseudorandom number generator that produces numbers X that are uniformly distributed in the interval [0,1]. These random variates X are then transformed via some algorithm to create a new random variate having the required probability distribution.

5 - Demo

The below graphic was generated with Javascript. Click on the “Try the code” to see the code.

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data/type/number/pseudo-random.txt · Last modified: 2019/09/14 11:37 by gerardnico