Hi, I am applying Monte Carlo for a problem involving mixed deterministic and random values. In order to avoid a lot of special handling and corner cases, I am using using numpy arrays full of a single value to represent the deterministic quantities.
Anyway, I found that the standard deviation turns out to be non-zero when these deterministic sets take on large values, which is wrong. This is due to machine precision loss. It turns out to be fairly straightforward to check for this situation upfront. See attached code. I've also shown a more accurate algorithm for computing the mean, but it adds an additional multiplication for every term in the sum, which is obviously undesirable from a performance perspective. Would it make sense to automatically detect the precision loss and use the more accurate approach when that is the case? If that seems ok, I can take a look at the numpy code, and submit a patch. Best wishes, Mike
mean-problem
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