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The generator computes an odd 128-bit value and returns its upper 64 bits. This generator passes BigCrush from TestU01, but fails the TMFn test from PractRand. That test has been designed to catch exactly the defect of this type of generator: since the modulus is a power of 2, the period of the lowest bit in the output is only 2 62, rather than ...
Lehmer generator: 1951 D. H. Lehmer [2] One of the very earliest and most influential designs. Linear congruential generator (LCG) 1958 W. E. Thomson; A. Rotenberg [3] [4] A generalisation of the Lehmer generator and historically the most influential and studied generator. Lagged Fibonacci generator (LFG) 1958 G. J. Mitchell and D. P. Moore [5]
In September 1949, he presented the pseudorandom number generator now known as the Lehmer random number generator. [4] D. H. Lehmer wrote the article "The Machine Tools of Combinatorics," which is the first chapter in Edwin Beckenbach's Applied Combinatorial Mathematics (1964). [5] It describes methods for producing permutations, combinations, etc.
A structure similar to LCGs, but not equivalent, is the multiple-recursive generator: X n = (a 1 X n−1 + a 2 X n−2 + ··· + a k X n−k) mod m for k ≥ 2. With a prime modulus, this can generate periods up to m k −1, so is a useful extension of the LCG structure to larger periods.
Thus, a multiply-with-carry generator is a Lehmer generator with modulus p and multiplier b −1 (mod p). This is the same as a generator with multiplier b, but producing output in reverse order, which does not affect the quality of the resultant pseudorandom numbers.
The Lehmer generator, designed by Derrick Henry Lehmer in the 1940s, has been foundational in the development of pseudo-random number generation. Its simplicity and efficiency in generating sequences make it an attractive choice, especially in fields like simulations, statistical sampling, and cryptography.
Lagged Fibonacci generator; Lehmer random number generator; Linear congruential generator; Linear-feedback shift register; M. Marsaglia polar method; Mersenne Twister;
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 ...