Pseudorandom number generator

A deterministic algorithm that generates numbers resembling random samples. Given the same internal state and inputs, it produces the same sequence.

Seed (randomness): An initial value that determines the starting state of a pseudorandom number generator. Changing the seed selects a different sequence; reusing it reproduces the same one.
Monte Carlo method: A computational method that uses random sampling to estimate quantities or solve problems. Pseudorandom samples make many simulations reproducible and computationally practical.
Random variable: A variable whose possible values are associated with probabilities. Generators commonly encode samples intended to behave like draws from specified random variables.
Hardware random number generator: A device that derives random values from measured physical processes. Unlike a conventional pseudorandom generator, it can obtain fresh randomness from physical phenomena.
State (computer science): The information a computational system retains to determine its future behavior. A generator updates its internal state at each step to produce successive outputs.
Cryptography: The study and practice of securing information through mathematical techniques. Cryptographic protocols depend on unpredictable values, so ordinary generators can create serious vulnerabilities.
Probability distribution: A mathematical description of the probabilities assigned to possible outcomes. A generator's output is judged partly by whether its values follow the intended distribution.
True random number generator: A system that produces random values from a nondeterministic physical or other entropy source. It contrasts with sequences fixed entirely by an algorithm and its initial state.
Linear congruential generator: A generator that updates an integer state using a linear recurrence modulo a fixed number. Its simple recurrence illustrates how deterministic arithmetic can produce a sequence with apparent irregularity.
Computer simulation: The use of a computational model to imitate the behavior of a real or hypothetical system. Simulations use generated samples to represent uncertain events and variable inputs.

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