In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use.
Good statistical properties are a central requirement for the output of a PRNG. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. for procedural generation), and cryptography. for the Monte Carlo method), electronic games (e.g. PRNGs are central in applications such as simulations (e.g. Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility.
The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed (which may include truly random values).
For the formal concept in theoretical computer science, see Pseudorandom generator.Ī pseudorandom number generator ( PRNG), also known as a deterministic random bit generator ( DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. This page is about commonly encountered characteristics of pseudorandom number generator algorithms.
0 kommentar(er)
