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Generating Uniform and Non-Uniform Random Deviates By brian kolo & aaron winters
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Applied science often examines physical situations that obey non-uniform statistical distributions. Many applications use the normal distribution as an approximation. However, in cases of simulating... More > behavior, the normal approximation may not be appropriate because a normally-distributed random variable may range from negative infinity to infinity. This wide range of values may represent unphysical situations and may lead to unpredictable behavior in a simulated environment. We examine the generation of random deviates according to both uniform and non-uniform probability distributions. A classification scheme for uniform random number generation is considered as well as an examination of the NIST suite of randomness testing. Techniques for generating non-uniform deviates are considered as well including change of variables, inverse distribution, the rejection method, tabular methods, and multivariate methods. Each of the methods is considered along with a number of examples of applying these techniques.< Less

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