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Moments of large datasets utilise the mean of the dataset; consequently,updating the dataset traditionally requires one to update the mean, which thenrequires one to recalculate the moment. This means that metrics such as thestandard deviation, $R^2$ correlation, and other statistics have to be`refreshed' for dataset updates, requiring large data storage and taking longtimes to process. Here, a method is shown for updating moments that onlyrequires the previous moments (which are computationally cheaper to store), andthe new data to be appended. This leads to a dramatic decrease in data storagerequirements, and significant computational speed-up for large datasets orlow-order moments (n $\lesssim$ 10).

Fast post-hoc method for updating moments of large datasets