In short, seed 0 gives exactly the same random numbers as seed 5489 in MATLAB (unless you use their deprecated rand(twister,0) syntax).One common work-flow is to run the same program hundreds of times where only the seed differs between runs.
This is probably good enough to ensure that each simulation uses a random number stream that is statistically independent from all of the others There is a risk that some streams will overlap but the probability is low and most people are content to live with that risk. Mersenne Twister Numpy Generator That GuaranteesAlternatively, move to a random number generator that guarantees non-overlapping, independent streams something that any implementation of Mersenne Twister cannot do. As expected the two seeds led to different sequences of random numbers. I now know for sure that if I generate more than 2N numbers in a stream, I am going to have problems. So, I just need to make sure I generate less than 2N. Does the routine keep track of the number of calls Ive made to it, and warn if Ive made too many, or do I need to do it manually. Mersenne Twister Numpy Full Access ToIt provides full access to the entire RNG state, all 634 32-bit integers. This is because if a user-specified seed has low entropy (likely since there are 630 values to be supplied), it is highly likely to set the generator to an apparently-low-entropy part of the sequence.
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