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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?
The text was updated successfully, but these errors were encountered:
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 computingM_1
,M_2
, ...,M_{n-1}
, similarly to what happens with our variance method inndarray
(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
?The text was updated successfully, but these errors were encountered: