What is your favorite "best practice" when designing and implementing computational models? Mine is: As far as possible, isolate stochasticity.
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Basically, if you run a unit test on some stochastic code and it fails, my package freezes the PRNG state so that on the next run, it's identical. Making failing tests deterministically reproducible is very useful.
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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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