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Interesting suggestion, but I'm not sure I agree with you. You could use that argument about any of the random numbers that your rng returns. "Hey, 0.023 is infinitesimally unlikely to occur, so let's exclude that as well". Generally when I use a real-valued rng, the numbers generated are meant to be 'representative' of what I should get from the distribution. And when I get e.g. 0.023, it kind-of means "0.023 and/or numbers near to 0.023". If I excluded 0.023 from the possible results of the rng, then I would have a 'hole' in my distribution 'in the region of' 0.023. Maybe what you want is to go from the possible rnds: 0.000, 0.001, 0.002, 0.003, ... , 0.999 to: 0.0005, 0.0015, 0.0025, ... , 0.9995 whereas with your suggestion, you are under-representing the boundary numbers near to 0 and/or near to 1. Generally not a problem I guess, but what you are doing is, imo, 'wrong in theory', even though your high-precision floats will probably cover it up ok in almost all cases. I agree with you about 0 and 1 causing problems when they are fed into other functions - e.g. generating Gaussian rngs by using InverseCumulativeGaussian(0.0) - a bug I wasted some time hunting down in my company's rng library. My view was that the developer who wrote the library did not understand the maths of what he was doing, rather than that the 0to1 random number generator was at fault. |