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Higher order moments #22

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LukeMathWalker opened this issue Jan 14, 2019 · 1 comment
Closed

Higher order moments #22

LukeMathWalker opened this issue Jan 14, 2019 · 1 comment

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@LukeMathWalker
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LukeMathWalker commented Jan 14, 2019

Moving forward with #1, I am now working towards computing central order moments. As far as I can understand, it's impossible to compute M_n in a numerically stable fashion without computing M_1, M_2, ..., M_{n-1}, similarly to what happens with our variance method in ndarray (I am using this as reference).

Should we make this transparent and return the whole array of moments up to the order required?
This would probably save some computational workload if people actually need more than one of those (e.g. mean, std deviation, kurtosis and skewness).

What do you think @jturner314?
The only issue is what do you we use as return type? A Vec?

@LukeMathWalker
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In the end I opted to use a corrected two-pass algorithm, hence I am returning just the n central moment. I'll close this.

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