PLine- >SetDatas(vLeftPositions, vDistributions) Vector vLeftPositions ( histo.GetLeftContainers() ) These are computed by two static functions, RNOR and REXP:Ĭopy Code vector vDistribution ( histo.GetNormalizedHistogram() ) The CRandomGenerator provides two types of random variables: normal and exponential. The algorithm used to compute the mean, variance and other statistical properties are taken from the numerical recipies (see ). A very nice web site on this topic can be found at Virtual Laboratories in Probability and Statistics. There are plenty of tutorials and demo applets on normal distributions on the web so I won't go into mathematical details here. The normal distribution is also called the Gaussian distribution, in honor of Carl Friedrich Gauss, who was among the first to use the distribution. The normal distribution holds an honored role in probability and statistics, mostly because of the central limit theorem, one of the fundamental theorems that forms a bridge between the two subjects. In order to illustrate the generator, a Histogram template class, THistogram is provided.
They did 99,9% of the work, I just encapsulated their C code in a class wrapper. Some 99% of the time, the required x is produced by: Generate a random 32-bit integer j and let i be the index formed from the rightmost 8 bits of j. It uses two tables, integers ki and reals wi. It is faster and simpler than the original, and will produce, for example, normal or exponential variates at the rate of 15 million per second with a C version on a 400MHz PC. We provide a new version of our ziggurat method for generating a random variable from a given decreasing density. The algorithm is due to George Marsaglia and Wai Wan Tsang in. This article presents a fast generator for Random Variable, namely normal and exponential distributions.
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