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When building simulations of real-world phenomena, or when generating test data for algorithms that will be consuming information from the real world, it is often highly desirable to produce pseudo-random data that conform to some nonuniform probability distribution. But perhaps I have already lost some readers who do not remember STATS 101 all those years ago. I sure don't. Let's take a step back. The .NET Framework conveniently provides you with a pseudo-random number generator that produces a uniform distribution of doubles between zero and one. By a "uniform distribution" we mean that if you took a
whole lot of those random numbers and put them in two buckets, based on whether they were greater than one half or smaller than one half, then you would expect to see roughly half the numbers in each bucket; there is no bias towards either side. But moreover: if you took those same numbers and put them in ten buckets, based on whether they were in the range 0.0-0.1, 0.1-0.2, 0.2-0.3, and so on, you would also expect to see no particular bias towards any of those buckets either. In fact, no matter how many buckets of uniform size you...(Read whole news on source site)



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