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(The term "quote generator" can also be used for software that randomly selects real quotations.) Further to its esoteric interest, a discussion of parody generation as a useful technique for measuring the success of grammatical inferencing systems is included, along with suggestions for its practical application in areas of language modeling ...
Pages in category "Random text generation" The following 19 pages are in this category, out of 19 total. This list may not reflect recent changes. A. AI Dungeon; B.
The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto (松本 眞) and Takuji Nishimura (西村 拓士). [1] [2] Its name derives from the choice of a Mersenne prime as its period length.
AutoNumbers generated by this mechanism start with the start number and increment with the increment value, checking for collision with existing table rows. [2] random AutoNumbers generated by this mechanism are assigned using a pseudo-random number generator that generates long integers and checks for collisions with existing table rows. [2]
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator.. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
Blum Blum Shub takes the form + =, where M = pq is the product of two large primes p and q.At each step of the algorithm, some output is derived from x n+1; the output is commonly either the bit parity of x n+1 or one or more of the least significant bits of x n+1.
To illustrate, imagine if a simple 32 bit linear congruential pseudo-random number generator of the type supplied with most programming languages (e.g., as the 'rand' or 'rnd' function) is used as a source of keys. There will only be some four billion possible values produced before the generator repeats itself.