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Abstract The paper is devoted to the complexity analysis of binary floating point pseudorandom generators. We start with a stochastic model of a “usual” pseudorandom generator (PRNG). Then integer outputs of this generator are transformed into i.i.d. Random variables, agreed with an abstract binary floating point system. Additionally, these random variables are approximately uniformly distributed on the interval [0,1].

Therefore, they can interpreted as (random) outputs of a binary floating point pseudorandom generator (flPRNG). The simulation complexity of such a transformation is defined as the average number of PRNG's outputs necessary to produce the unique output of flPRNG. Several transformations with minimal or approximately minimal complexities are presented and discussed. Keywords:;; MSC:.

The Complexity Of Nonuniform Random Number Generation Pdf Editor. HP Journal - online issues. 2Cover: the PCX- S chipset for the Apollo 9. Should be of high precision, this method also requires a large number of random bits. An implementation of the discrete distribution generating (DDG) tree [11]. Pierrot Dictators Circus A Deformed Bud Rarlab. Performs sampling from non-uniform distributions using a near-optimal number of random bits. A detailed comparative analysis of different sampling methods.