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Telephone number representation. Canadian (and other North American Numbering Plan) telephone numbers are usually written as NPA-NXX-XXXX. For example, 250 555 0199, a fictional number, could be written as (250) 555-0199, 250-555-0199, 250-5550199, or 250/555-0199.
Random number generators are important in many kinds of technical applications, including physics, engineering or mathematical computer studies (e.g., Monte Carlo simulations), cryptography and gambling (on game servers).
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. This means that the particular outcome sequence will contain some patterns detectable in hindsight but impossible to foresee.
M. Marsaglia polar method. Mersenne Twister. Middle-square method. MIXMAX generator. Multiply-with-carry pseudorandom number generator.
A Million Random Digits with 100,000 Normal Deviates. MIXMAX generator.
A counter-based random number generation ( CBRNG, also known as a counter-based pseudo-random number generator, or CBPRNG) is a kind of pseudorandom number generator that uses only an integer counter as its internal state. They are generally used for generating pseudorandom numbers for large parallel computations.
RDRAND (for "read random") is an instruction for returning random numbers from an Intel on-chip hardware random number generator which has been seeded by an on-chip entropy source. It is also known as Intel Secure Key Technology, codenamed Bull Mountain.
In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), or physical random number generator is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a physical entropy source ...
A 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.
If one has a pseudo-random number generator whose output is "sufficiently difficult" to predict, one can generate true random numbers to use as the initial value (i.e., the seed), and then use the pseudo-random number generator to produce numbers for use in cryptographic applications.