What is your favorite "best practice" when designing and implementing computational models? Mine is: As far as possible, isolate stochasticity.
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Interesting. I think most statistical programmers I know fix the random seed for tests. We learn to do it in class assignments so the grader can reproduce our stuff.
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Yea, I think that's the common training. My problem with that is that stochastic "fuzzing" is useful for uncovering bugs. Sometimes, your fixed seed generates realizations that are unfortunate w.r.t. a specific test. Quickcheck-style testing removes this risk...
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