1. The Complexity Of Nonuniform Random Number Generation Pdf Converter Pdf

.Part of thebook series (LNCS, volume 4943) AbstractMonte Carlo financial simulation relies on the generation of random variables with different probability distribution functions. These simulations, particularly the random number generator (RNG) cores, are computationally intensive and are ideal candidates for hardware acceleration. In this work we present an FPGA based Log-normal RNG ideally suited for financial Monte Carlo simulations, as it is run-time parameterisable and compatible with variance reduction techniques.

Our architecture achieves a throughput of one sample per cycle with a 227.6 MHz clock on a Xilinx Virtex-4 FPGA.

What distribution of deviates do you want?Here is a technique which always works, but isn't always the most efficient. The cumulative distrubtion function P(x) gives the fraction of the time that values fall below x.

The Complexity Of Nonuniform Random Number Generation Pdf Converter

The Complexity Of Nonuniform Random Number Generation Pdf Converter Pdf

Thus P(x)=0 at the lowest possible value of x and P(x)=1 at the highest possible value of x. Every distribution has a unique CDF, which encodes all the properties of the distrubtion in the way that P(x) rises from 0 to 1. If y is a uniform deviate on the interval 0,1, then x satisfying P(x)=y will be disributed according to your distribution. To make this work comuptationally, you just need a way computing the inverse of P(x) for your distribution.The library defines a large number of commonly used distrubtions (e.g. Normal, lognormal, exponential, chi squared, etc.) and has functions for computing the CDF (Distribution.LeftProbability) and the inverse CDF (Distribution.InverseLeftProbability) of each.For specialized techniques that are fast for particular distrubtions, e.g. The Box-Muller technique for normaly distributed deviates, see the book Numerical Recipies.

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