Some applications which appear at first sight to be suitable for randomization are in fact not quite so simple. Weaker forms of randomness are used in hash algorithms and in creating amortized searching and sorting algorithms. Some simple examples might be presenting a user with a "random quote of the day", or determining which way a computer-controlled adversary might move in a computer game. While cryptography and certain numerical algorithms require a very high degree of apparent randomness, many other operations only need a modest amount of unpredictability.
The generation of pseudorandom numbers is an important and common task in computer programming. Sender and receiver can generate the same set of numbers automatically to use as keys. They are also used in cryptography – so long as the seed is secret. Pseudorandom number generators are very useful in developing Monte Carlo-method simulations, as debugging is facilitated by the ability to run the same sequence of random numbers again by starting from the same random seed. Generally, in applications having unpredictability as the paramount feature, such as in security applications, hardware generators are generally preferred over pseudorandom algorithms, where feasible. Random number generators have applications in gambling, statistical sampling, computer simulation, cryptography, completely randomized design, and other areas where producing an unpredictable result is desirable. However, carefully designed cryptographically secure pseudorandom number generators (CSPRNGS) also exist, with special features specifically designed for use in cryptography. This generally makes them unusable for applications such as cryptography. All fall short of the goal of true randomness, although they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible). Several computational methods for pseudorandom number generation exist. Thus, results would sometimes be collected and distributed as random number tables. Because of the mechanical nature of these techniques, generating large quantities of sufficiently random numbers (important in statistics) required much work and time. Some of these have existed since ancient times, including well-known examples like the rolling of dice, coin flipping, the shuffling of playing cards, the use of yarrow stalks (for divination) in the I Ching, as well as countless other techniques. Various applications of randomness have led to the development of different methods for generating random data. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs), which generate numbers that only look random but are in fact pre-determined-these generations can be reproduced simply by knowing the state of the PRNG. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight.
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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. When a cubical die is rolled, a random number from 1 to 6 is obtained. JSTOR ( June 2009) ( Learn how and when to remove this template message)ĭice are an example of a mechanical hardware random number generator.
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